Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus

Stimulation artefact removal: review and evaluation of applications in evoked responses

Objective.This study investigated software methods for removing stimulation artefacts in recordings undertaken during deep brain stimulation (DBS). We aimed to evaluate artefact attenuation using sample recordings of evoked resonant neural activity (ERNA), as well as a synthetic ground-truth waveform that emulated observed ERNA characteristics.Approach.The synthetic waveform and eight raw DBS recordings were processed by fourteen algorithms spanning the following categories: signal modification, signal decomposition, and template subtraction. For the synthetic waveform, performance was quantified by comparing each reconstructed signal against the ground-truth waveform. For DBS recordings, performance was contrasted amongst each other. The stimulation artefact was quantified by its amplitude and subsequent decay to baseline by the time to first zero-crossing. Each reconstructed ERNA signal was characterised by peak-to-peak-amplitude, root-mean-square amplitude, latency, and number of zero-crossings.Main results.None of the methods performed overall as well as the Backward Filter. Signal decomposition techniques were able to attenuate stimulation artefact albeit with unacceptable ERNA distortion.Significance.Upon evaluation of common software methods for DBS artefact attenuation, we advocate the use of the Backward Filter for reducing such artefacts while reconstructing ERNA.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

BACKGROUND

Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis.

NEW METHOD

In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered.

RESULT

SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates.

COMPARISON WITH EXISTING METHODS

SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering.

CONCLUSION

The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection

Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

A novel framework for the removal of pacing artifacts from bio-electrical recordings

BACKGROUND

Electroceuticals provide clinical solutions for a range of disorders including Parkinson's disease, cardiac arrythmias and are emerging as a potential treatment option for gastrointestinal disorders. However, pre-clinical investigations are challenged by the large stimulation artifacts registered in bio-electrical recordings.

METHOD

A generalized framework capable of isolating and suppressing stimulation artifacts with minimal intervention was developed. Stimulation artifacts with different pulse-parameters in synthetic and experimental cardiac and gastrointestinal signals were detected using a Hampel filter and reconstructed using 3 methods: i) autoregression, ii) weighted mean, and iii) linear interpolation.

RESULTS

Synthetic stimulation artifacts with amplitudes of 2 mV and 4 mV and pulse-widths of 50 ms, 100 ms, and 200 ms were successfully isolated and the artifact window size remained uninfluenced by the pulse-amplitude, but was influenced by pulse-width (e.g., the autoregression method resulted in an identical Root Mean Square Error (RMSE) of 1.64 mV for artifacts with 200 ms pulse-width and both 2 mV and 4 mV amplitudes). The performance of autoregression (RMSE = 1.45 ± 0.16 mV) and linear interpolation (RMSE = 1.22 ± 0.14 mV) methods were comparable and better than weighted mean (RMSE = 5.54 ± 0.56 mV) for synthetic data. However, for experimental recordings, artifact removal by autoregression was superior to both linear interpolation and weighted mean approaches in gastric, small intestinal and cardiac recordings.

CONCLUSIONS

A novel signal processing framework enabled efficient analysis of bio-electrical recordings with stimulation artifacts. This will allow the bio-electrical events induced by stimulation protocols to be efficiently and systematically evaluated, resulting in improved stimulation therapies.

Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research

Introduction

Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback.

Methods

A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system.

Results

The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R 2 > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R 2 > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses.

Significance

This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Low-cost stimulation resistant electromyography

Surface Electromyography (sEMG) is the non-invasive measurement of skeletal muscle contraction bio-potentials. Measuring sEMG of a stimulated muscle can prove particularly difficult due to large scale and long lasting stimulation-induced artefacts: if an sEMG device does not account for such artefacts, its measurements can be swamped and components damaged. sEMG has been used in a wide range of clinical and biomedical fields, providing measures such as muscular fatigue and subject intent. The recording of sEMG can prove difficult due to signal contamination such as movement artefact and mains interference. There are very few commercial sEMG devices that contain protection against large stimulation voltages or measures to reduce artefact transient times. Furthermore, most commercial or research level designs are not open source; these designs are effectively an inflexible black box to researchers and developers. This research presents the design, test and validation of an open source sEMG design, able to record muscle bio-potentials concurrently to electrical stimulation. The open source, low-cost nature of the design provides accessibility to researchers without the time and cost associated with design development. The design has been tested on the forearms of four able-bodied subjects during 25 Hz constant current stimulation, and has been shown to record subject volitional sEMG and M-wave without saturation.

Artefact-free recording of local field potentials with simultaneous stimulation for closed-loop Deep-Brain Stimulation

Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However, closed-loop DBS based on LFPs requires simultaneous recording and stimulating, which remains a challenge due to persistent stimulation artefacts that distort underlying LFP biomarkers. Here we first investigate the nature of the stimulation-induced artefacts and review several techniques that have been proposed to deal with them. Then we propose a new method to synchronize the sampling clock with the stimulation pulse so that the stimulation artefacts are never sampled, while at the same time the Nyquist-Shannon theorem is satisfied for uninterrupted LFP recording. Test results show that this method achieves true uninterrupted artefact-free LFP recording over a wide frequency band and for a wide range of stimulation frequencies.Clinical relevance-The method proposed here provides continuous and artefact-free recording of LFPs close to the stimulation target, and thereby facilitates the implementation of more advanced closed-loop DBS using LFPs as feedback.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

A high-performance 4 nV (√Hz)-1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

OBJECTIVE

Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts.

APPROACH

To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted.

MAIN RESULTS

A high-performance, 4 nV ([Formula: see text])^-1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5-250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter.

SIGNIFICANCE

The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.

A Miniature Dual-Biomarker-Based Sensing and Conditioning Device for Closed-Loop DBS

In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.

A Programmable Multi-biomarker Neural Sensor for Closed-loop DBS

Most of the current closed-loop DBS devices use a single biomarker in their feedback loop which may limit their performance and applications. This paper presents design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7-45 Hz), and high-frequency (200-1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range 50-100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving preamplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: (i) bench tests using sinusoidal and pre-recorded neural signals, (ii) in-vitro tests using pre-recorded neural signals in saline solution, and (iii) in-vivo tests by recording neural signals from freely-moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

OBJECTIVE

Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data.

APPROACH

Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity.

MAIN RESULTS

SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain.

SIGNIFICANCE

This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

Online Artifact Cancelation in Same-Electrode Neural Stimulation and Recording Using a Combined Hardware and Software Architecture

Advancing studies of neural network dynamics and developments of closed-loop neural interfaces requires the ability to simultaneously stimulate and record the neural cells. Recording adjacent to or at the stimulation site produces artifact signals that are orders of magnitude larger than the neural responses of interest. These signals often saturate the recording amplifier causing distortion or loss of short-latency evoked responses. This paper proposes a method to cancel the artifact in simultaneous neural recording and stimulation on the same electrode. By combining a novel hardware architecture with concurrent software processing, the design achieves neural signal recovery in a wide range of conditions. The proposed system uniquely demonstrates same-electrode stimulation and recording, with neural signal recovery in presence of stimulation artifact 100 dB larger in magnitude than the underlying signals. The system is tested both in vitro and in vivo, during concurrent stimulation and recording on the same electrode. In vivo results in a rodent model are compared to recordings made by a commercial neural amplifier system connected in parallel.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

METHODS

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

RESULTS

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

CONCLUSIONS

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Cortical neurostimulation for neuropathic pain: state of the art and perspectives

The treatment of neuropathic pain by neuromodulation is an objective for more than 40 years in modern clinical practice. With respect to spinal cord and deep brain structures, the cerebral cortex is the most recently evaluated target of invasive neuromodulation therapy for pain. In the early 90s, the first successes of invasive epidural motor cortex stimulation (EMCS) were published. A few years later was developed repetitive transcranial magnetic stimulation (rTMS), a noninvasive stimulation technique. Then, electrical transcranial stimulation returned valid and is currently in full development, with transcranial direct current stimulation (tDCS). Regarding transcranial approaches, the main studied and validated target was still the motor cortex, but other cortical targets are under investigation. The mechanisms of action of these techniques share similarities, especially between EMCS and rTMS, but they also have differences that could justify specific indications and applications. It is therefore important to know the principles and to assess the merit of these techniques on the basis of a rigorous assessment of the results, to avoid fad. Various types of chronic neuropathic pain syndromes can be significantly relieved by EMCS or repeated daily sessions of high-frequency (5-20 Hz) rTMS or anodal tDCS over weeks, at least when pain is lateralized and stimulation is applied to the motor cortex contralateral to pain side. However, cortical stimulation therapy remains to be optimized, especially by improving EMCS electrode design, rTMS targeting, or tDCS montage, to reduce the rate of nonresponders, who do not experience clinically relevant effects of these techniques.

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.

Bilateral thalamic stimulation induces insomnia in patients treated for intractable tremor

STUDY OBJECTIVES

To explore the influence of acute bilateral ventral intermediate thalamic nucleus (VIM) stimulation on sleep.

DESIGN

Three consecutive full-night polysomnography recordings were made in the laboratory. After the habituation night, a random order for night ON-stim and OFF-stim was applied for the second and third nights.

SETTING

Sleep disorders unit of a university hospital.

PATIENTS

Eleven patients with bilateral stimulation of the ventral intermediate nucleus of the thalamus (VIM) for drug-resistant tremor.

MEASUREMENTS

Sleep measures on polysomnography.

RESULTS

Total sleep time was reduced during night ON-stim compared to OFF- stim, as well as rapid eye movement sleep percentage while the percentage of N2 increased. Wakefulness after sleep onset time was increased.

CONCLUSION

Our results show that bilateral stimulation of the VIM nuclei reduces sleep and could be associated with insomnia.

Suppression of stimulus artifact contaminating electrically evoked electromyography

BACKGROUND

Electrical stimulation of muscle or nerve is a very useful technique for understanding of muscle activity and its pathological changes for both diagnostic and therapeutic purposes. During electrical stimulation of a muscle, the recorded M wave is often contaminated by a stimulus artifact. The stimulus artifact must be removed for appropriate analysis and interpretation of M waves.

OBJECTIVES

The objective of this study was to develop a novel software based method to remove stimulus artifacts contaminating or superimposing with electrically evoked surface electromyography (EMG) or M wave signals.

METHODS

The multiple stage method uses a series of signal processing techniques, including highlighting and detection of stimulus artifacts using Savitzky-Golay filtering, estimation of the artifact contaminated region with Otsu thresholding, and reconstruction of such region using signal interpolation and smoothing. The developed method was tested using M wave signals recorded from biceps brachii muscles by a linear surface electrode array. To evaluate the performance, a series of semi-synthetic signals were constructed from clean M wave and stimulus artifact recordings with different degrees of overlap between them.

RESULTS

The effectiveness of the developed method was quantified by a significant increase in correlation coefficient and a significant decrease in root mean square error between the clean M wave and the reconstructed M wave, compared with those between the clean M wave and the originally contaminated signal. The validity of the developed method was also demonstrated when tested on each channel's M wave recording using a linear electrode array.

CONCLUSIONS

The developed method can suppress stimulus artifacts contaminating M wave recordings.

Closed-loop cortical neuromodulation in Parkinson's disease: An alternative to deep brain stimulation?

Deep brain stimulation (DBS) is usually performed to treat advanced Parkinson's disease (PD) patients with electrodes permanently implanted in basal ganglia while the stimulator delivers electrical impulses continuously and independently of any feedback (open-loop stimulation). Conversely, in closed-loop stimulation, electrical stimulation is delivered as a function of neuronal activities recorded and analyzed online. There is an emerging development of closed-loop DBS in the treatment of PD and a growing discussion about proposing cortical stimulation rather than DBS for this purpose. Why does it make sense to "close the loop" to treat parkinsonian symptoms? Could closed-loop stimulation applied to the cortex become a valuable therapeutic strategy for PD? Can mathematical modeling contribute to the development of this technique? We review the various evidences in favor of the use of closed-loop cortical stimulation for the treatment of advanced PD, as an emerging technique which might offer substantial clinical benefits for PD patients.

Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths

Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

Motor cortex stimulation inhibits thalamic sensory neurons and enhances activity of PAG neurons: possible pathways for antinociception

Motor cortex stimulation is generally suggested as a therapy for patients with chronic and refractory neuropathic pain. However, the mechanisms underlying its analgesic effects are still unknown. In a previous study, we demonstrated that cortical stimulation increases the nociceptive threshold of naive conscious rats with opioid participation. In the present study, we investigated the neurocircuitry involved during the antinociception induced by transdural stimulation of motor cortex in naive rats considering that little is known about the relation between motor cortex and analgesia. The neuronal activation patterns were evaluated in the thalamic nuclei and midbrain periaqueductal gray. Neuronal inactivation in response to motor cortex stimulation was detected in thalamic sites both in terms of immunolabeling (Zif268/Fos) and in the neuronal firing rates in ventral posterolateral nuclei and centromedian-parafascicular thalamic complex. This effect was particularly visible for neurons responsive to nociceptive peripheral stimulation. Furthermore, motor cortex stimulation enhanced neuronal firing rate and Fos immunoreactivity in the ipsilateral periaqueductal gray. We have also observed a decreased Zif268, δ-aminobutyric acid (GABA), and glutamic acid decarboxylase expression within the same region, suggesting an inhibition of GABAergic interneurons of the midbrain periaqueductal gray, consequently activating neurons responsible for the descending pain inhibitory control system. Taken together, the present findings suggest that inhibition of thalamic sensory neurons and disinhibition of the neurons in periaqueductal gray are at least in part responsible for the motor cortex stimulation-induced antinociception.

Towards a suitable time-scale representation of cardio-respiratory signals through Empirical Mode Decomposition algorithms: a simulation and validation tool

To what extent is Empirical Mode Decomposition (EMD) able to differentiate the embedded components of a cardio-respiratory (CR) signal? We intend to answer this question by providing a tool which compares the performances of the original EMD algorithm with those of a noise-assisted version (CEEMD) on simulated CR signals, depending on the frequency and amplitude ratios between their respiratory and cardiac components. A statistical Bland & Altman test checks the matching of stroke volumes calculated from the extracted cardiac signal and those from the simulated one. CEEMD turns out to be better than EMD by yielding to reliable multiscale representation of simulated CR signals on a wider domain of frequency and amplitude ratios.