Multi-scale analysis of neural activity in humans: Implications for micro-scale electrocorticography

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

Wafer-patterned, permeable, and stretchable liquid metal microelectrodes for implantable bioelectronics with chronic biocompatibility

Implantable bioelectronics provide unprecedented opportunities for real-time and continuous monitoring of physiological signals of living bodies. Most bioelectronics adopt thin-film substrates such as polyimide and polydimethylsiloxane that exhibit high levels of flexibility and stretchability. However, the low permeability and relatively high modulus of these thin films hamper the long-term biocompatibility. In contrast, devices fabricated on porous substrates show the advantages of high permeability but suffer from low patterning density. Here, we report a wafer-scale patternable strategy for the high-resolution fabrication of supersoft, stretchable, and permeable liquid metal microelectrodes (μLMEs). We demonstrate 2-μm patterning capability, or an ultrahigh density of ~75,500 electrodes/cm2, of μLME arrays on a wafer-size (diameter, 100 mm) elastic fiber mat by photolithography. We implant the μLME array as a neural interface for high spatiotemporal mapping and intervention of electrocorticography signals of living rats. The implanted μLMEs have chronic biocompatibility over a period of eight months.

Nine decades of electrocorticography: A comparison between epidural and subdural recordings

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

A novel method for dynamically altering the surface area of intracranial EEG electrodes

Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

Time stability and connectivity analysis with an intracortical 96-channel microelectrode array inserted in human visual cortex

OBJECTIVE

Microstimulation via electrodes that penetrate the visual cortex creates visual perceptions called phosphenes. Besides providing electrical stimulation to induce perceptions, each electrode can be used to record the brain signals from the cortex region under the electrode which contains brain state information. Since the future visual prosthesis interfaces will be implanted chronically in the visual cortex of blind people, it is important to study the long-term stability of the signals acquired from the electrodes. Here, we studied the changes over time and the repercussions of electrical stimulation on the brain signals acquired with an intracortical 96-channel microelectrode array implanted in the visual cortex of a blind volunteer for 6 months.

APPROACH

We used variance, power spectral density, correlation, coherence, and phase coherence to study the brain signals acquired in resting condition before and after the administration of electrical stimulation during a period of 6 months.

MAIN RESULTS

Variance and power spectral density up to 750 Hz do not show any significant trend in the 6 months, but correlation coherence and phase coherence significantly decrease over the implantation time and increase after electrical stimulation.

SIGNIFICANCE

The stability of variance and power spectral density in time is important for long-term clinical applications based on the intracortical signals collected by the electrodes. The decreasing trends of correlation, coherence, and phase coherence might be related to plasticity changes in the visual cortex due to electrical microstimulation.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Vitamin C-reduced graphene oxide improves the performance and stability of multimodal neural microelectrodes

Nanocarbons are often employed as coatings for neural electrodes to enhance surface area. However, processing and integrating them into microfabrication flows requires complex and harmful chemical and heating conditions. This article presents a safe, scalable, cost-effective method to produce reduced graphene oxide (rGO) coatings using vitamin C (VC) as the reducing agent. We spray coat GO + VC mixtures onto target substrates, and then heat samples for 15 min at 150°C. The resulting rGO films have conductivities of ∼44 S cm⁻¹, and are easily integrated into an ad hoc microfabrication flow. The rGO/Au microelectrodes show ∼8x lower impedance and ∼400x higher capacitance than bare Au, resulting in significantly enhanced charge storage and injection capacity. We subsequently use rGO/Au arrays to detect dopamine in vitro, and to map cortical activity intraoperatively over rat whisker barrel cortex, demonstrating that conductive VC-rGO coatings improve the performance and stability of multimodal microelectrodes for different applications.

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Easy, scalable, and safe reduction method to create rGO films with vitamin C

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VC-rGO coatings improve the performance of bare gold microelectrodes in vitro

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VC-rGO coatings enable the voltammetric detection of dopamine on the microscale

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rGO/Au electrode arrays enable high-resolution microscale recording in vivo

The Listening Zone of Human Electrocorticographic Field Potential Recordings

Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.

Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques

Objective: Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning tools to improve the motor decoding accuracy at the level of individual fingers. Approach: We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art machine learning algorithms on the BCI competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, 9 subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p<0.01) and regression tasks (p<0.01). Main results: Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (6-class task, including rest state), improving over the state-of-the-art conditional random fields (CRF) by 11.7% on the 3 BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson's correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN+LSTM). Furthermore, our proposed method features a low time complexity, with only <17.2s required for training and <50ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance. Significance: The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.

Recent Advances and Current Trends in Brain-Computer Interface (BCI) Research and Their Applications

Brain-Computer Interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, one hundred most cited articles from the WOS database were selected over the last four years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

Nanofibrous PEDOT-Carbon Composite on Flexible Probes for Soft Neural Interfacing

In this study, we report a flexible implantable 4-channel microelectrode probe coated with highly porous and robust nanocomposite of poly (3,4-ethylenedioxythiophene) (PEDOT) and carbon nanofiber (CNF) as a solid doping template for high-performance in vivo neuronal recording and stimulation. A simple yet well-controlled deposition strategy was developed via in situ electrochemical polymerization technique to create a porous network of PEDOT and CNFs on a flexible 4-channel gold microelectrode probe. Different morphological and electrochemical characterizations showed that they exhibit remarkable and superior electrochemical properties, yielding microelectrodes combining high surface area, low impedance (16.8 ± 2 MΩ µm² at 1 kHz) and elevated charge injection capabilities (7.6 ± 1.3 mC/cm²) that exceed those of pure and composite PEDOT layers. In addition, the PEDOT-CNF composite electrode exhibited extended biphasic charge cycle endurance and excellent performance under accelerated lifetime testing, resulting in a negligible physical delamination and/or degradation for long periods of electrical stimulation. In vitro testing on mouse brain slices showed that they can record spontaneous oscillatory field potentials as well as single-unit action potentials and allow to safely deliver electrical stimulation for evoking field potentials. The combined superior electrical properties, durability and 3D microstructure topology of the PEDOT-CNF composite electrodes demonstrate outstanding potential for developing future neural surface interfacing applications.

Thin-film microfabrication and intraoperative testing ofµECoG and iEEG depth arrays for sense and stimulation

Objective.Intracranial neural recordings and electrical stimulation are tools used in an increasing range of applications, including intraoperative clinical mapping and monitoring, therapeutic neuromodulation, and brain computer interface control and feedback. However, many of these applications suffer from a lack of spatial specificity and localization, both in terms of sensed neural signal and applied stimulation. This stems from limited manufacturing processes of commercial-off-the-shelf (COTS) arrays unable to accommodate increased channel density, higher channel count, and smaller contact size.Approach.Here, we describe a manufacturing and assembly approach using thin-film microfabrication for 32-channel high density subdural micro-electrocorticography (µECoG) surface arrays (contacts 1.2 mm diameter, 2 mm pitch) and intracranial electroencephalography (iEEG) depth arrays (contacts 0.5 mm × 1.5 mm, pitch 0.8 mm × 2.5 mm). Crucially, we tackle the translational hurdle and test these arrays during intraoperative studies conducted in four humans under regulatory approval.Main results.We demonstrate that the higher-density contacts provide additional unique information across the recording span compared to the density of COTS arrays which typically have electrode pitch of 8 mm or greater; 4 mm in case of specially ordered arrays. Our intracranial stimulation study results reveal that refined spatial targeting of stimulation elicits evoked potentials with differing spatial spread.Significance.Thin-film,μECoG and iEEG depth arrays offer a promising substrate for advancing a number of clinical and research applications reliant on high-resolution neural sensing and intracranial stimulation.

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees

BACKGROUND AND OBJECTIVE

Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions.

METHODS

The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition.

RESULTS

The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space.

CONCLUSION

This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates

Long-lasting, high-resolution neural interfaces that are ultrathin and flexible are essential for precise brain mapping and high-performance neuroprosthetic systems. Scaling to sample thousands of sites across large brain regions requires integrating powered electronics to multiplex many electrodes to a few external wires. However, existing multiplexed electrode arrays rely on encapsulation strategies that have limited implant lifetimes. Here, we developed a flexible, multiplexed electrode array, called "Neural Matrix," that provides stable in vivo neural recordings in rodents and nonhuman primates. Neural Matrix lasts over a year and samples a centimeter-scale brain region using over a thousand channels. The long-lasting encapsulation (projected to last at least 6 years), scalable device design, and iterative in vivo optimization described here are essential components to overcoming current hurdles facing next-generation neural technologies.

Flexible, high-resolution thin-film electrodes for human and animal neural research

Objective.Brain functions such as perception, motor control, learning, and memory arise from the coordinated activity of neuronal assemblies distributed across multiple brain regions. While major progress has been made in understanding the function of individual neurons, circuit interactions remain poorly understood. A fundamental obstacle to deciphering circuit interactions is the limited availability of research tools to observe and manipulate the activity of large, distributed neuronal populations in humans. Here we describe the development, validation, and dissemination of flexible, high-resolution, thin-film (TF) electrodes for recording neural activity in animals and humans.Approach.We leveraged standard flexible printed-circuit manufacturing processes to build high-resolution TF electrode arrays. We used biocompatible materials to form the substrate (liquid crystal polymer; LCP), metals (Au, PtIr, and Pd), molding (medical-grade silicone), and 3D-printed housing (nylon). We designed a custom, miniaturized, digitizing headstage to reduce the number of cables required to connect to the acquisition system and reduce the distance between the electrodes and the amplifiers. A custom mechanical system enabled the electrodes and headstages to be pre-assembled prior to sterilization, minimizing the setup time required in the operating room. PtIr electrode coatings lowered impedance and enabled stimulation. High-volume, commercial manufacturing enables cost-effective production of LCP-TF electrodes in large quantities.Main Results. Our LCP-TF arrays achieve 25× higher electrode density, 20× higher channel count, and 11× reduced stiffness than conventional clinical electrodes. We validated our LCP-TF electrodes in multiple human intraoperative recording sessions and have disseminated this technology to >10 research groups. Using these arrays, we have observed high-frequency neural activity with sub-millimeter resolution.Significance.Our LCP-TF electrodes will advance human neuroscience research and improve clinical care by enabling broad access to transformative, high-resolution electrode arrays.

High-density mapping of primate digit representations with a 1152-channelµECoG array

Objective.Advances in brain-machine interfaces (BMIs) are expected to support patients with movement disorders. Electrocorticogram (ECoG) measures electrophysiological activities over a large area using a low-invasive flexible sheet placed on the cortex. ECoG has been considered as a feasible signal source of the clinical BMI device. To capture neural activities more precisely, the feasibility of higher-density arrays has been investigated. However, currently, the number of electrodes is limited to approximately 300 due to wiring difficulties, device size, and system costs.Approach.We developed a high-density recording system with a large coverage (14 × 7 mm²) and using 1152 electrodes by directly integrating dedicated flexible arrays with the neural-recording application-specific integrated circuits and their interposers.Main results.Comparative experiments with a 128-channel array demonstrated that the proposed device could delineate the entire digit representation of a nonhuman primate. Subsampling analysis revealed that higher-amplitude signals can be measured using higher-density arrays.Significance.We expect that the proposed system that simultaneously establishes large-scale sampling, high temporal-precision of electrophysiology, and high spatial resolution comparable to optical imaging will be suitable for next-generation brain-sensing technology.

Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG

Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites.Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at≤10% error for most cases (95% of observations).Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas.Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.

Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and Non-convex Optimization

OBJECTIVE

Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed Simultaneous Analysis Non-Convex Optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording.

APPROACH

The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers (ADMM) is developed for multi-channel LFPs reconstruction.

MAIN RESULTS

Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency.

SIGNIFICANCE

Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.

A soft and stretchable bilayer electrode array with independent functional layers for the next generation of brain machine interfaces

OBJECTIVE

Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable).

APPROACH

Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system.

MAIN RESULTS

To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol.

SIGNIFICANCE

The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array (lahrμECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.

A modular high-density μECoG system on macaque vlPFC for auditory cognitive decoding

OBJECTIVE

A fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC). Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC's contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability in order to identify the information that could be decoded from μECoG vlPFC signals.

APPROACH

We molded three separate μECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on the left hemisphere. The molded 294-contact μECoG array was implanted subdurally over the vlPFC. μECoG activity was recorded while the monkey participated in a 'hearing-in-noise' task in which they reported hearing a 'target' vocalization from a background 'chorus' of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr).

MAIN RESULTS

We decoded the TCr and the monkey's behavioral choices from the μECoG signal. We analyzed decoding accuracy as a function of number of electrodes, spatial resolution, and time from implantation. Over a one-year period, we found significant decoding with individual electrodes that increased significantly as we decoded simultaneously more electrodes. Further, we found that the decoding for behavioral choice was better than the decoding of TCr. Finally, because the decoding accuracy of individual electrodes varied on a day-by-day basis, electrode arrays with high channel counts ensure robust decoding in the long term.

SIGNIFICANCE

Our results demonstrate the utility of high-resolution and high-channel-count, chronic µECoG recording. We developed a surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

Materials for flexible bioelectronic systems as chronic neural interfaces

Engineered systems that can serve as chronically stable, high-performance electronic recording and stimulation interfaces to the brain and other parts of the nervous system, with cellular-level resolution across macroscopic areas, are of broad interest to the neuroscience and biomedical communities. Challenges remain in the development of biocompatible materials and the design of flexible implants for these purposes, where ulimate goals are for performance attributes approaching those of conventional wafer-based technologies and for operational timescales reaching the human lifespan. This Review summarizes recent advances in this field, with emphasis on active and passive constituent materials, design architectures and integration methods that support necessary levels of biocompatibility, electronic functionality, long-term stable operation in biofluids and reliability for use in vivo. Bioelectronic systems that enable multiplexed electrophysiological mapping across large areas at high spatiotemporal resolution are surveyed, with a particular focus on those with proven chronic stability in live animal models and scalability to thousands of channels over human-brain-scale dimensions. Research in materials science will continue to underpin progress in this field of study.

Context-dependent relationship in high-resolution micro-ECoG studies during finger movements

OBJECTIVE

The activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.

METHODS

Three neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.

RESULTS

In all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).

CONCLUSIONS

HG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

Conformable Hybrid Systems for Implantable Bioelectronic Interfaces

Conformable bioelectronic systems are promising tools that may aid the understanding of diseases, alleviate pathological symptoms such as chronic pain, heart arrhythmia, and dysfunctions, and assist in reversing conditions such as deafness, blindness, and paralysis. Combining reduced invasiveness with advanced electronic functions, hybrid bioelectronic systems have evolved tremendously in the last decade, pushed by progress in materials science, micro- and nanofabrication, system assembly and packaging, and biomedical engineering. Hybrid integration refers here to a technological approach to embed within mechanically compliant carrier substrates electronic components and circuits prepared with traditional electronic materials. This combination leverages mechanical and electronic performance of polymer substrates and device materials, respectively, and offers many opportunities for man-made systems to communicate with the body with unmet precision. However, trade-offs between materials selection, manufacturing processes, resolution, electrical function, mechanical integrity, biointegration, and reliability should be considered. Herein, prominent trends in manufacturing conformable hybrid systems are analyzed and key design, function, and validation principles are outlined together with the remaining challenges to produce reliable conformable, hybrid bioelectronic systems.

Mapping of primary somatosensory cortex of the hand area using a high-density electrocorticography grid for closed-loop brain computer interface

The ideal modality for generating sensation in sensorimotor brain computer interfaces (BCI) has not been determined. Here we report the feasibility of using a high-density "mini"-electrocorticography (mECoG) grid in a somatosensory BCI system. Thirteen subjects with intractable epilepsy underwent standard clinical implantation of subdural electrodes for the purpose of seizure localization. An additional high-density mECoG grid was placed (Adtech, 8 by 8, 1.2-mm exposed, 3-mm center-to-center spacing) over the hand area of primary somatosensory cortex. Following implantation, cortical mapping was performed with stimulation parameters of frequency: 50 Hz, pulse-width: 250 µs, pulse duration: 4 s, polarity: alternating, and current that ranged from 0.5 mA to 12 mA at the discretion of the epileptologist. Location of the evoked sensory percepts was recorded along with a description of the sensation. The hand was partitioned into 48 distinct boxes. A box was included if sensation was felt anywhere within the box. The percentage of the hand covered was 63.9% (± 34.4%) (mean ± s.d.). Mean redundancy, measured as electrode pairs stimulating the same box, was 1.9 (± 2.2) electrodes per box; and mean resolution, measured as boxes included per electrode pair stimulation, was 11.4 (± 13.7) boxes with 8.1 (± 10.7) boxes in the digits and 3.4 (± 6.0) boxes in the palm. Functional utility of the system was assessed by quantifying usable percepts. Under the strictest classification, "dermatomally exclusive" percepts, the mean was 2.8 usable percepts per grid. Allowing "perceptually unique" percepts at the same anatomical location, the mean was 5.5 usable percepts per grid. Compared to the small area of coverage and redundancy of a microelectrode system, or the poor resolution of a standard ECoG grid, a mECoG is likely the best modality for a somatosensory BCI system with good coverage of the hand and minimal redundancy.

The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions

Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.

Decoding Movement From Electrocorticographic Activity: A Review

Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

Electrocorticographic Encoding of Human Gait in the Leg Primary Motor Cortex

While prior noninvasive (e.g., electroencephalographic) studies suggest that the human primary motor cortex (M1) is active during gait processes, the limitations of noninvasive recordings make it impossible to determine whether M1 is involved in high-level motor control (e.g., obstacle avoidance, walking speed), low-level motor control (e.g., coordinated muscle activation), or only nonmotor processes (e.g., integrating/relaying sensory information). This study represents the first invasive electroneurophysiological characterization of the human leg M1 during walking. Two subjects with an electrocorticographic grid over the interhemispheric M1 area were recruited. Both exhibited generalized γ-band (40-200 Hz) synchronization across M1 during treadmill walking, as well as periodic γ-band changes within each stride (across multiple walking speeds). Additionally, these changes appeared to be of motor, rather than sensory, origin. However, M1 activity during walking shared few features with M1 activity during individual leg muscle movements, and was not highly correlated with lower limb trajectories on a single channel basis. These findings suggest that M1 primarily encodes high-level gait motor control (i.e., walking duration and speed) instead of the low-level patterns of leg muscle activation or movement trajectories. Therefore, M1 likely interacts with subcortical/spinal networks, which are responsible for low-level motor control, to produce normal human walking.

Visual epidural field potentials possess high functional specificity in single trials

Recordings of epidural field potentials (EFPs) allow neuronal activity to be acquired over a large region of cortical tissue with minimal invasiveness. Because electrodes are placed on top of the dura and do not enter the neuronal tissue, EFPs offer intriguing options for both clinical and basic science research. On the other hand, EFPs represent the integrated activity of larger neuronal populations and possess a higher trial-by-trial variability and a reduced signal-to-noise ratio due the additional barrier of the dura. It is thus unclear whether and to what extent EFPs have sufficient spatial selectivity to allow for conclusions about the underlying functional cortical architecture, and whether single EFP trials provide enough information on the short timescales relevant for many clinical and basic neuroscience purposes. We used the high spatial resolution of primary visual cortex to address these issues and investigated the extent to which very short EFP traces allow reliable decoding of spatial information. We briefly presented different visual objects at one of nine closely adjacent locations and recorded neuronal activity with a high-density epidural multielectrode array in three macaque monkeys. With the use of receiver operating characteristics (ROC) to identify the most informative data, machine-learning algorithms provided close-to-perfect classification rates for all 27 stimulus conditions. A binary classifier applying a simple max function on ROC-selected data further showed that single trials might be classified with 100% performance even without advanced offline classifiers. Thus, although highly variable, EFPs constitute an extremely valuable source of information and offer new perspectives for minimally invasive recording of large-scale networks.NEW & NOTEWORTHY Epidural field potential (EFP) recordings provide a minimally invasive approach to investigate large-scale neural networks, but little is known about whether they possess the required specificity for basic and clinical neuroscience. By making use of the spatial selectivity of primary visual cortex, we show that single-trial information can be decoded with close-to-perfect performance, even without using advanced classifiers and based on very few data. This labels EFPs as a highly attractive and widely usable signal.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise

BACKGROUND

Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

NEW METHOD

Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

RESULTS

The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications.

COMPARISON WITH EXISTING METHOD(S)

We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods.

CONCLUSIONS

The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.

Correlation Structure in Micro-ECoG Recordings is Described by Spatially Coherent Components

Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.

Electrocorticography (ECoG) is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain. ECoG is a promising technique for studying the brain, and EcoG signals can be used to control brain-computer interfaces. Advances have made it possible to record simultaneously with an increasing number of smaller, and more closely spaced electrodes. However, a property of electrical recording from outside the brain is that common signals appear on different electrodes at different locations, and this affects decisions about how to best distribute a limited number of electrodes to maximize the information that can be gathered. Large spacing of electrodes around one centimeter apart on the brain’s surface has proven useful for clinical and research use, but how much benefit there is to recording from more locations in a smaller area remains to be answered. We found that we can explain the commonality between the different locations as the combination of different patterns of brain activity that are present at multiple electrode locations, and that signals recorded from very closely spaced electrodes, around a millimeter or less apart, are able to identify patterns that are at this small scale.

Technical considerations for generating somatosensation via cortical stimulation in a closed-loop sensory/motor brain-computer interface system in humans

Somatosensory feedback is the next step in brain computer interface (BCI). Here, we compare three cortical stimulating array modalities for generating somatosensory percepts in BCI. We compared human subjects with either a 64-channel "mini"-electrocorticography grid (mECoG; 1.2-mm diameter exposed contacts with 3-mm spacing, N = 1) over the hand area of primary somatosensory cortex (S1), or a standard grid (sECoG; 1.5-mm diameter exposed contacts with 1-cm spacing, N = 1), to generate artificial somatosensation through direct electrical cortical stimulation. Finally, we reference data in the literature from a patient implanted with microelectrode arrays (MEA) placed in the S1 hand area. We compare stimulation results to assess coverage and specificity of the artificial percepts in the hand. Using the mECoG array, hand mapping revealed coverage of 41.7% of the hand area versus 100% for the sECoG array, and 18.8% for the MEA. On average, stimulation of a single electrode corresponded to sensation reported in 4.42 boxes (range 1-11 boxes) for the mECoG array, 19.11 boxes (range 4-48 boxes) for the sECoG grid, and 2.3 boxes (range 1-5 boxes) for the MEA. Sensation in any box, on average, corresponded to stimulation from 2.65 electrodes (range 1-5 electrodes) for the mECoG grid, 3.58 electrodes for the sECoG grid (range 2-4 electrodes), and 11.22 electrodes (range 2-17 electrodes) for the MEA. Based on these findings, we conclude that mECoG grids provide an excellent balance between spatial cortical coverage of the hand area of S1 and high-density resolution.

Progress in the Field of Micro-Electrocorticography

Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain⁻computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain⁻computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.

The influence of prior pronunciations on sensorimotor cortex activity patterns during vowel production

OBJECTIVE

In recent years, brain-computer interface (BCI) systems have been investigated for their potential as a communication device to assist people with severe paralysis. Decoding speech sensorimotor cortex activity is a promising avenue for the generation of BCI control signals, but is complicated by variability in neural patterns, leading to suboptimal decoding. We investigated whether neural pattern variability associated with sound pronunciation can be explained by prior pronunciations and determined to what extent prior speech affects BCI decoding accuracy.

APPROACH

Neural patterns in speech motor areas were evaluated with electrocorticography in five epilepsy patients, who performed a simple speech task that involved pronunciation of the /i/ sound, preceded by either silence, the /a/ sound or the /u/ sound.

MAIN RESULTS

The neural pattern related to the /i/ sound depends on previous sounds and is therefore associated with multiple distinct sensorimotor patterns, which is likely to reflect differences in the movements towards this sound. We also show that these patterns still contain a commonality that is distinct from the other vowel sounds (/a/ and /u/). Classification accuracies for the decoding of different sounds do increase, however, when the multiple patterns for the /i/ sound are taken into account. Simply including multiple forms of the /i/ vowel in the training set for the creation of a single /i/ model performs as well as training individual models for each /i/ variation.

SIGNIFICANCE

Our results are of interest for the development of BCIs that aim to decode speech sounds from the sensorimotor cortex, since they argue that a multitude of cortical activity patterns associated with speech movements can be reduced to a basis set of models which reflect meaningful language units (vowels), yet it is important to account for the variety of neural patterns associated with a single sound in the training process.

Long-term recording reliability of liquid crystal polymer µECoG arrays

OBJECTIVE

The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability of the implanted interfaces.

APPROACH

We evaluated the chronic reliability of novel 61-channel micro-electrocorticographic (µECoG) arrays in rats chronically implanted for over one year and using accelerated aging. Devices were encapsulated with polyimide (PI) or liquid crystal polymer (LCP), and fabricated using commercial manufacturing processes. In vitro failure modes and predicted lifetimes were determined from accelerated soak testing. Successful designs were implanted epidurally over the rodent auditory cortex. Trends in baseline signal level, evoked responses and decoding performance were reported for over one year of implantation.

MAIN RESULTS

Devices fabricated with LCP consistently had longer in vitro lifetimes than PI encapsulation. Our accelerated aging results predicted device integrity beyond 3.4 years. Five implanted arrays showed stable performance over the entire implantation period (247-435 d). Our regression analysis showed that impedance predicted signal quality and information content only in the first 31 d of recordings and had little predictive value in the chronic phase (>31 d). In the chronic phase, site impedances slightly decreased yet decoding performance became statistically uncorrelated with impedance. We also employed an improved statistical model of spatial variation to measure sensitivity to locally varying fields, which is typically concealed in standard signal power calculations.

SIGNIFICANCE

These findings show that µECoG arrays can reliably perform in chronic applications in vivo for over one year, which facilitates the development of a high-density, clinically viable interface.

What does scalp electroencephalogram coherence tell us about long-range cortical networks?

Long-range interactions between cortical areas are undoubtedly a key to the computational power of the brain. For healthy human subjects, the premier method for measuring brain activity on fast timescales is electroencephalography (EEG), and coherence between EEG signals is often used to assay functional connectivity between different brain regions. However, the nature of the underlying brain activity that is reflected in EEG coherence is currently the realm of speculation, because seldom have EEG signals been recorded simultaneously with intracranial recordings near cell bodies in multiple brain areas. Here, we take the early steps towards narrowing this gap in our understanding of EEG coherence by measuring local field potentials with microelectrode arrays in two brain areas (extrastriate visual area V4 and dorsolateral prefrontal cortex) simultaneously with EEG at the nearby scalp in rhesus macaque monkeys. Although we found inter-area coherence at both scales of measurement, we did not find that scalp-level coherence was reliably related to coherence between brain areas measured intracranially on a trial-to-trial basis, despite that scalp-level EEG was related to other important features of neural oscillations, such as trial-to-trial variability in overall amplitudes. This suggests that caution must be exercised when interpreting EEG coherence effects, and new theories devised about what aspects of neural activity long-range coherence in the EEG reflects.

Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex

The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain–computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75–135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.

GridLoc: An automatic and unsupervised localization method for high-density ECoG grids

Precise localization of electrodes is essential in the field of high-density (HD) electrocorticography (ECoG) brain signal analysis in order to accurately interpret the recorded activity in relation to functional anatomy. Current localization methods for subchronically implanted HD electrode grids involve post-operative imaging. However, for situations where post-operative imaging is not available, such as during acute measurements in awake surgery, electrode localization is complicated. Intra-operative photographs may be informative, but not for electrode grids positioned partially or fully under the skull. Here we present an automatic and unsupervised method to localize HD electrode grids that does not require post-operative imaging. The localization method, named GridLoc, is based on the hypothesis that the anatomical and vascular brain structures under the ECoG electrodes have an effect on the amplitude of the recorded ECoG signal. More specifically, we hypothesize that the spatial match between resting-state high-frequency band power (45-120 Hz) patterns over the grid and the anatomical features of the brain under the electrodes, such as the presence of sulci and larger blood vessels, can be used for adequate HD grid localization. We validate this hypothesis and compare the GridLoc results with electrode locations determined with post-operative imaging and/or photographs in 8 patients implanted with HD-ECoG grids. Locations agreed with an average difference of 1.94 ± 0.11 mm, which is comparable to differences reported earlier between post-operative imaging and photograph methods. The results suggest that resting-state high-frequency band activity can be used for accurate localization of HD grid electrodes on a pre-operative MRI scan and that GridLoc provides a convenient alternative to methods that rely on post-operative imaging or intra-operative photographs.

Optimization of sampling rate and smoothing improves classification of high frequency power in electrocorticographic brain signals

Objective

High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings.

Approach

With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients.

Main results

The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1-0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4-0.6 s and 0.9-2.7 s).

Significance

The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.

Sub-millimeter ECoG pitch in human enables higher fidelity cognitive neural state estimation

Electrocorticography (ECoG), electrophysiological recording from the pial surface of the brain, is a critical measurement technique for clinical neurophysiology, basic neurophysiology studies, and demonstrates great promise for the development of neural prosthetic devices for assistive applications and the treatment of neurological disorders. Recent advances in device engineering are poised to enable orders of magnitude increase in the resolution of ECoG without comprised measurement quality. This enhancement in cortical sensing enables the observation of neural dynamics from the cortical surface at the micrometer scale. While these technical capabilities may be enabling, the extent to which finer spatial scale recording enhances functionally relevant neural state inference is unclear. We examine this question by employing a high-density and low impedance 400 μm pitch microECoG (μECoG) grid to record neural activity from the human cortical surface during cognitive tasks. By applying machine learning techniques to classify task conditions from the envelope of high-frequency band (70-170Hz) neural activity collected from two study participants, we demonstrate that higher density grids can lead to more accurate binary task condition classification. When controlling for grid area and selecting task informative sub-regions of the complete grid, we observed a consistent increase in mean classification accuracy with higher grid density; in particular, 400 μm pitch grids outperforming spatially sub-sampled lower density grids up to 23%. We also introduce a modeling framework to provide intuition for how spatial properties of measurements affect the performance gap between high and low density grids. To our knowledge, this work is the first quantitative demonstration of human sub-millimeter pitch cortical surface recording yielding higher-fidelity state estimation relative to devices at the millimeter-scale, motivating the development and testing of μECoG for basic and clinical neurophysiology as well as towards the realization of high-performance neural prostheses.

New thin-film surface electrode array enables brain mapping with high spatial acuity in rodents

In neuroscience, single-shank penetrating multi-electrode arrays are standard for sequentially sampling several cortical sites with high spatial and temporal resolution, with the disadvantage of neuronal damage. Non-penetrating surface grids used in electrocorticography (ECoG) permit simultaneous recording of multiple cortical sites, with limited spatial resolution, due to distance to neuronal tissue, large contact size and high impedances. Here we compared new thin-film parylene C ECoG grids, covering the guinea pig primary auditory cortex, with simultaneous recordings from penetrating electrode array (PEAs), inserted through openings in the grid material. ECoG grid local field potentials (LFP) showed higher response thresholds and amplitudes compared to PEAs. They enabled, however, fast and reliable tonotopic mapping of the auditory cortex (place-frequency slope: 0.7 mm/octave), with tuning widths similar to PEAs. The ECoG signal correlated best with supragranular layers, exponentially decreasing with cortical depth. The grids also enabled recording of multi-unit activity (MUA), yielding several advantages over LFP recordings, including sharper frequency tunings. ECoG first spike latency showed highest similarity to superficial PEA contacts and MUA traces maximally correlated with PEA recordings from the granular layer. These results confirm high quality of the ECoG grid recordings and the possibility to collect LFP and MUA simultaneously.

ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids

BACKGROUND

Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT.

NEW METHOD

ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab^®, thus providing an easy, automated and standard tool for ECoG electrode localization.

RESULTS

ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method.

COMPARISON WITH EXISTING METHODS

A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects.

CONCLUSIONS

The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.

Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids

For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high-density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain-Computer Interfaces are discussed.

Physiological properties of brain-machine interface input signals

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm². The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Clinical applications of penetrating neural interfaces and Utah Electrode Array technologies

This paper briefly describes some of the recent progress in the development of penetrating microelectrode arrays and highlights the use of two of these devices, Utah electrode arrays and Utah slanted electrode arrays, in two therapeutic interventions: recording volitional skeletal motor commands from the central nervous system, and recording motor commands and evoking somatosensory percepts in the peripheral nervous system (PNS). The paper also briefly explores other potential sites for microelectrode array interventions that could be profitably pursued and that could have important consequences in enhancing the quality of life of patients that has been compromised by disorders of the central and PNSs.

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

OBJECTIVE

Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram.

APPROACH

Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient.

MAIN RESULTS

The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies.

SIGNIFICANCE

Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures

The extensive distribution and simultaneous termination of seizures across cortical areas has led to the hypothesis that seizures are caused by large-scale coordinated networks spanning these areas. This view, however, is difficult to reconcile with most proposed mechanisms of seizure spread and termination, which operate on a cellular scale. We hypothesize that seizures evolve into self-organized structures wherein a small seizing territory projects high-intensity electrical signals over a broad cortical area. Here we investigate human seizures on both small and large electrophysiological scales. We show that the migrating edge of the seizing territory is the source of travelling waves of synaptic activity into adjacent cortical areas. As the seizure progresses, slow dynamics in induced activity from these waves indicate a weakening and eventual failure of their source. These observations support a parsimonious theory for how large-scale evolution and termination of seizures are driven from a small, migrating cortical area.

Epileptic brains display inhibitory restraint as manifested by the spread of synchronized activities being delayed in timing. Here, Elliot Smith and colleagues show fast-moving traveling wave that originates from the edge of ictal wavefront with subsequent depolarization and multiunit firing in the seizing brain regions in epileptic patients.