Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications

Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study

Shifts in spatial attention are associated with variations in α band (α, 8-14 Hz) activity, specifically in interhemispheric imbalance. The underlying mechanism is attributed to local α-synchronization, which regulates local inhibition of neural excitability, and frontoparietal synchronization reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCIs). In the present study, we explored whether long-range α-synchronization presents lateralized patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data show a lateralized pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronization with support vector machines (SVMs) was at chance level. The present findings suggest EEG may not be capable of detecting long-range α-synchronization in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.

Independent bilateral-eye stimulation for gaze pattern recognition based on steady-state pupil light reflex

Objective:recently, pupil oscillations synchronized with steady visual stimuli were used as input for an interface. The proposed system, inspired by a brain-computer interface based on steady-state visual evoked potentials, does not require contact with the participant. However, the pupil oscillation mechanism limits the stimulus frequency to 2.5 Hz or less, making it hard to enhance the information transfer rate (ITR).Approach:here, we compared multiple conditions for stimulation to increase the ITR of the pupil vibration-based interface, which were called monocular-single, monocular-superposed, and binocular-independent conditions. The binocular-independent condition stimulates each eye at different frequencies respectively and mixes them by using the visual stereoscopic perception of users. The monocular-superposed condition stimulates both eyes by a mixed signal of two different frequencies. We selected the shape of the stimulation signal, evaluated the amount of spectral leakage in the monocular-superposed and binocular-independent conditions, and compared the power spectrum density at the stimulation frequency. Moreover, 5, 10, and 15 patterns of stimuli were classified in each condition.Main results:a square wave, which causes an efficient pupil response, was used as the stimulus. Spectral leakage at the beat frequency was higher in the monocular-superposed condition than in the binocular-independent one. The power spectral density of stimulus frequencies was greatest in the monocular-single condition. Finally, we could classify the 15-stimulus pattern, with ITRs of 14.4 (binocular-independent, using five frequencies), 14.5 (monocular-superimposed, using five frequencies), and 23.7 bits min^-1(monocular-single, using 15 frequencies). There were no significant differences for the binocular-independent and monocular-superposed conditions.Significance:this paper shows a way to increase the number of stimuli that can be simultaneously displayed without decreasing ITR, even when only a small number of frequencies are available. This could lead to the provision of an interface based on pupil oscillation to a wider range of users.

Tracking momentary fluctuations in human attention with a cognitive brain-machine interface

Selective attention produces systematic effects on neural states. It is unclear whether, conversely, momentary fluctuations in neural states have behavioral significance for attention. We investigated this question in the human brain with a cognitive brain-machine interface (cBMI) for tracking electrophysiological steady-state visually evoked potentials (SSVEPs) in real-time. Discrimination accuracy (d') was significantly higher when target stimuli were triggered at high, versus low, SSVEP power states. Target and distractor SSVEP power was uncorrelated across the hemifields, and target d' was unaffected by distractor SSVEP power states. Next, we trained participants on an auditory neurofeedback paradigm to generate biased, cross-hemispheric competitive interactions between target and distractor SSVEPs. The strongest behavioral effects emerged when competitive SSVEP dynamics unfolded at a timescale corresponding to the deployment of endogenous attention. In sum, SSVEP power dynamics provide a reliable readout of attentional state, a result with critical implications for tracking and training human attention.

Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex

A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.

Distractibility and impulsivity neural states are distinct from selective attention and modulate the implementation of spatial attention

In the context of visual attention, it has been classically assumed that missing the response to a target or erroneously selecting a distractor occurs as a consequence of the (miss)allocation of attention in space. In the present paper, we challenge this view and provide evidence that, in addition to encoding spatial attention, prefrontal neurons also encode a distractibility-to-impulsivity state. Using supervised dimensionality reduction techniques in prefrontal neuronal recordings in monkeys, we identify two partially overlapping neuronal subpopulations associated either with the focus of attention or overt behaviour. The degree of overlap accounts for the behavioral gain associated with the good allocation of attention. We further describe the neural variability accounting for distractibility-to-impulsivity behaviour by a two dimensional state associated with optimality in task and responsiveness. Overall, we thus show that behavioral performance arises from the integration of task-specific neuronal processes and pre-existing neuronal states describing task-independent behavioral states.

Failing to detect relevant information has been assumed to be a consequence of misallocation of attention. Here, the authors present findings showing that optimal behavioral performance results from the absence of interference between internal neural states and attention control.

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface

Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-computer interface (BCI) however existing cBCI systems currently offer lower accuracy than the motor BCI. Since attention is one of the cognitive signals that can be used to realise the cBCI, this work uses the multiple object tracking (MOT) task to acquire the desired electroencephalograph (EEG) signal from healthy subjects. The main objective of the paper is to explore the preliminary applications of support vector machine (SVM) classifier to classify the attentional load in multiple object tracking task. Results show that the attentional load can be classified using SVM with sensitivity, specificity, and accuracy of 94.03%, 92.50%, and 93.28%, respectively using the spectral entropy EEG feature. The classification performance promises the potential application of the current approach in the cognitive brain-computer interface for neurorehabilitation.

Prefrontal Control of Proactive and Reactive Mechanisms of Visual Suppression

In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by suppressing the ongoing processing of distractors. The distinctive neuronal signature of these suppressive mechanisms is still largely unknown. Thanks to machine-learning decoding methods applied to prefrontal cortical activity, we monitor the dynamic spatial attention with an unprecedented spatial and temporal resolution. We first identify independent behavioral and neuronal signatures for long-term (learning-based spatial prioritization) and short-term (dynamic spatial attention) mechanisms. We then identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on the implementation of both long-term and short-term mechanisms of selection. Overall, we provide a unified neuro-cognitive framework describing how the prefrontal cortex deals with distractors in order to flexibly optimize behavior in dynamic environments.

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

Interface, interaction, and intelligence in generalized brain-computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

Time-resolved estimation of strength of motor imagery representation by multivariate EEG decoding

Objective. Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored.Approach.In this study, we addressed this question by applying a support vector machine (SVM) to extract motor imagery (MI) representations, from electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand.Main results.Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a hierarchical genetic algorithm for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages.Significance.Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.

Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

The neural bases of spatial attention and perceptual rhythms

Attentional processes allow the brain to overcome its processing capacities limitations by enhancing relevant visual information and suppressing irrelevant information. Thus attention plays a critical role, shaping our perception of the world. Several models have been proposed to describe the neuronal bases of attention and its mechanistic underlyings. Recent electrophysiological evidence show that attentional processes rely on oscillatory brain activities that correlate with rhythmic changes in cognitive performance. In the present review, we first take a historical perspective on how attention is viewed, from the initial spotlight theory of attention to the recent dynamic view of attention selection and we review their supporting psychophysical evidence. Based on recent prefrontal electrophysiological evidence, we refine the most recent models of attention sampling by proposing a rhythmic and continuous model of attentional sampling. In particular, we show that attention involves a continuous exploration of space, shifting within and across visual hemifield at specific alpha and theta rhythms, independently of the current attentional load. In addition, we show that this prefrontal attentional spotlight implements conjointly selection and suppression mechanisms, and is captured by salient incoming items. Last, we argue that this attention spotlight implements a highly flexible alternation of attentional exploration and exploitation epochs, depending on ongoing task contingencies. In a last part, we review the local and network oscillatory mechanisms that correlate with rhythmic attentional sampling, describing multiple rhythmic generators and complex network interactions.

Decoding of standard and non-standard visuomotor associations from parietal cortex

OBJECTIVE

Neural signals can be decoded and used to move neural prostheses with the purpose of restoring motor function in patients with mobility impairments. Such patients typically have intact eye movement control and visual function, suggesting that cortical visuospatial signals could be used to guide external devices. Neurons in parietal cortex mediate sensory-motor transformations, encode the spatial coordinates for reaching goals, hand position and movements, and other spatial variables. We studied how spatial information is represented at the population level, and the possibility to decode not only the position of visual targets and the plans to reach them, but also conditional, non-spatial motor responses.

APPROACH

The animals first fixated one of nine targets in 3D space and then, after the target changed color, either reached toward it, or performed a non-spatial motor response (lift hand from a button). Spiking activity of parietal neurons was recorded in monkeys during two tasks. We then decoded different task related parameters.

MAIN RESULTS

We first show that a maximum-likelihood estimation (MLE) algorithm trained separately in each task transformed neural activity into accurate metric predictions of target location. Furthermore, by combining MLE with a Naïve Bayes classifier, we decoded the monkey's motor intention (reach or hand lift) and the different phases of the tasks. These results show that, although V6A encodes the spatial location of a target during a delay period, the signals they carry are updated around the movement execution in an intention/motor specific way.

SIGNIFICANCE

These findings show the presence of multiple levels of information in parietal cortex that could be decoded and used in brain machine interfaces to control both goal-directed movements and more cognitive visuomotor associations.

Prefrontal attentional saccades explore space rhythmically

Recent studies suggest that attention samples space rhythmically through oscillatory interactions in the frontoparietal network. How these attentional fluctuations coincide with spatial exploration/displacement and exploitation/selection by a dynamic attentional spotlight under top-down control is unclear. Here, we show a direct contribution of prefrontal attention selection mechanisms to a continuous space exploration. Specifically, we provide a direct high spatio-temporal resolution prefrontal population decoding of the covert attentional spotlight. We show that it continuously explores space at a 7-12 Hz rhythm. Sensory encoding and behavioral reports are increased at a specific optimal phase w/ to this rhythm. We propose that this prefrontal neuronal rhythm reflects an alpha-clocked sampling of the visual environment in the absence of eye movements. These attentional explorations are highly flexible, how they spatially unfold depending both on within-trial and across-task contingencies. These results are discussed in the context of exploration-exploitation strategies and prefrontal top-down attentional control.

Brain-computer interfaces in neurologic rehabilitation practice

The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.

A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface

Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task.” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial.

(clinical-trial ID: NCT02445625—clinicaltrials.gov).

Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

Real-time decoding of covert attention in higher-order visual areas

Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.

Effects of neurofeedback on the short-term memory and continuous attention of patients with moderate traumatic brain injury: A preliminary randomized controlled clinical trial

PURPOSE

There are some studies which showed neurofeedback therapy (NFT) can be effective in clients with traumatic brain injury (TBI) history. However, randomized controlled clinical trials are still needed for evaluation of this treatment as a standard option. This preliminary study was aimed to evaluate the effect of NFT on continuous attention (CA) and short-term memory (STM) of clients with moderate TBI using a randomized controlled clinical trial (RCT).

METHODS

In this preliminary RCT, seventeen eligible patients with moderate TBI were randomly allocated in two intervention and control groups. All the patients were evaluated for CA and STM using the visual continuous attention test and Wechsler memory scale-4th edition (WMS-IV) test, respectively, both at the time of inclusion to the project and four weeks later. The intervention group participated in 20 sessions of NFT through the first four weeks. Conversely, the control group participated in the same NF sessions from the fifth week to eighth week of the project.

RESULTS

Eight subjects in the intervention group and five subjects in the control group completed the study. The mean and standard deviation of participants' age were (26.75 ± 15.16) years and (27.60 ± 8.17) years in experiment and control groups, respectively. All of the subjects were male. No significant improvement was observed in any variables of the visual continuous attention test and WMS-IV test between two groups (p ≥ 0.05).

CONCLUSION

Based on our literature review, it seems that our study is the only study performed on the effect of NFT on TBI patients with control group. NFT has no effect on CA and STM in patients with moderate TBI. More RCTs with large sample sizes, more sessions of treatment, longer time of follow-up and different protocols are recommended.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

Virtual reality and brain computer interface in neurorehabilitation

The potential benefit of technology to enhance recovery after central nervous system injuries is an area of increasing interest and exploration. The primary emphasis to date has been motor recovery/augmentation and communication. This paper introduces two original studies to demonstrate how advanced technology may be integrated into subacute rehabilitation. The first study addresses the feasibility of brain computer interface with patients on an inpatient spinal cord injury unit. The second study explores the validity of two virtual environments with acquired brain injury as part of an intensive outpatient neurorehabilitation program. These preliminary studies support the feasibility of advanced technologies in the subacute stage of neurorehabilitation. These modalities were well tolerated by participants and could be incorporated into patients' inpatient and outpatient rehabilitation regimens without schedule disruptions. This paper expands the limited literature base regarding the use of advanced technologies in the early stages of recovery for neurorehabilitation populations and speaks favorably to the potential integration of brain computer interface and virtual reality technologies as part of a multidisciplinary treatment program.

The Mind-Writing Pupil: A Human-Computer Interface Based on Decoding of Covert Attention through Pupillometry

We present a new human-computer interface that is based on decoding of attention through pupillometry. Our method builds on the recent finding that covert visual attention affects the pupillary light response: Your pupil constricts when you covertly (without looking at it) attend to a bright, compared to a dark, stimulus. In our method, participants covertly attend to one of several letters with oscillating brightness. Pupil size reflects the brightness of the selected letter, which allows us–with high accuracy and in real time–to determine which letter the participant intends to select. The performance of our method is comparable to the best covert-attention brain-computer interfaces to date, and has several advantages: no movement other than pupil-size change is required; no physical contact is required (i.e. no electrodes); it is easy to use; and it is reliable. Potential applications include: communication with totally locked-in patients, training of sustained attention, and ultra-secure password input.

Differential dynamics of spatial attention, position, and color coding within the parietofrontal network

Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network.

Deferoxamine attenuates lipopolysaccharide-induced neuroinflammation and memory impairment in mice

Background

Neuroinflammation often results in enduring cognitive impairment and is a risk factor for postoperative cognitive dysfunction. There are currently no effective treatments for infection-induced cognitive impairment. Previous studies have shown that the iron chelator deferoxamine (DFO) can increase the resistance of neurons to injury and disease by stimulating adaptive cellular stress responses. However, the impact of DFO on the cognitive sequelae of neuroinflammation is unknown.

Methods

A mouse model of lipopolysaccharide (LPS)-induced cognitive impairment was established to evaluate the neuroprotective effects of DFO against LPS-induced memory deficits and neuroinflammation. Adult C57BL/6 mice were treated with 0.5 μg of DFO 3 days prior to intracerebroventricular microinjection of 2 μg of LPS. Cognitive function was assessed using a Morris water maze from post-injection days 1 to 3. Animal behavioral tests, as well as pathological and biochemical assays were performed to evaluate the LPS-induced hippocampal damage and the neuroprotective effect of DFO.

Results

Treatment of mice with LPS resulted in deficits in cognitive performance in the Morris water maze without changing locomotor activity, which were ameliorated by pretreatment with DFO. DFO prevented LPS-induced microglial activation and elevations of IL-1β and TNF-α levels in the hippocampus. Moreover, DFO attenuated elevated expression of caspase-3, modulated GSK3β activity, and prevented LPS-induced increases of MDA and SOD levels in the hippocampus. DFO also significantly blocked LPS-induced iron accumulation and altered expression of proteins related to iron metabolism in the hippocampus.

Conclusions

Our results suggest that DFO may possess a neuroprotective effect against LPS-induced neuroinflammation and cognitive deficits via mechanisms involving maintenance of less brain iron, prevention of neuroinflammation, and alleviation of oxidative stress and apoptosis.

On the need to better specify the concept of "control" in brain-computer-interfaces/neurofeedback research

Aiming at a better specification of the concept of “control” in brain-computer-interfaces (BCIs) and neurofeedback (NF) research, we propose to distinguish “self-control of brain activity” from the broader concept of “BCI control”, since the first describes a neurocognitive phenomenon and is only one of the many components of “BCI control”. Based on this distinction, we developed a framework based on dual-processes theory that describes the cognitive determinants of self-control of brain activity as the interplay of automatic vs. controlled information processing. Further, we distinguish between cognitive processes that are necessary and sufficient to achieve a given level of self-control of brain activity and those which are not. We discuss that those cognitive processes which are not necessary for the learning process can hamper self-control because they cannot be completely turned-off at any time. This framework aims at a comprehensive description of the cognitive determinants of the acquisition of self-control of brain activity underlying those classes of BCI which require the user to achieve regulation of brain activity as well as NF learning.