Concurrent functional ultrasound imaging with graphene-based DC-coupled electrophysiology as a platform to study slow brain signals and cerebral blood flow under control and pathophysiological brain states

Current methodology used to investigate how shifts in brain states associated with regional cerebral blood volume (CBV) change in deep brain areas, are limited by either the spatiotemporal resolution of the CBV techniques, and/or compatibility with electrophysiological recordings; particularly in relation to spontaneous brain activity and the study of individual events. Additionally, infraslow brain signals (<0.1 Hz), including spreading depolarisations, DC-shifts and infraslow oscillations (ISO), are poorly captured by traditional AC-coupled electrographic recordings; yet these very slow brain signals can profoundly change CBV. To gain an improved understanding of how infraslow brain signals couple to CBV we present a new method for concurrent CBV with wide bandwidth electrophysiological mapping using simultaneous functional ultrasound imaging (fUS) and graphene-based field effect transistor (gFET) DC-coupled electrophysiological acquisitions. To validate the feasibility of this methodology visually-evoked neurovascular coupling (NVC) responses were examined. gFET recordings are not affected by concurrent fUS imaging, and epidural placement of gFET arrays within the imaging window did not deteriorate fUS signal quality. To examine directly the impact of infra-slow potential shifts on CBV, cortical spreading depolarisations (CSDs) were induced. A biphasic pattern of decreased, followed by increased CBV, propagating throughout the ipsilateral cortex, and a delayed decrease in deeper subcortical brain regions was observed. In a model of acute seizures, CBV oscillations were observed prior to seizure initiation. Individual seizures occurred on the rising phase of both infraslow brain signal and CBV oscillations. When seizures co-occurred with CSDs, CBV responses were larger in amplitude, with delayed CBV decreases in subcortical structures. Overall, our data demonstrate that gFETs are highly compatible with fUS and allow concurrent examination of wide bandwidth electrophysiology and CBV. This graphene-enabled technological advance has the potential to improve our understanding of how infraslow brain signals relate to CBV changes in control and pathological brain states.

Mapping of the central sulcus using non-invasive ultra-high-density brain recordings

Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.

Electrophysiological Characterization of Subclinical and Overt Hypertrophic Cardiomyopathy by Magnetic Resonance Imaging-Guided Electrocardiography

BACKGROUND

Ventricular arrhythmia in hypertrophic cardiomyopathy (HCM) relates to adverse structural change and genetic status. Cardiovascular magnetic resonance (CMR)-guided electrocardiographic imaging (ECGI) noninvasively maps cardiac structural and electrophysiological (EP) properties.

OBJECTIVES

The purpose of this study was to establish whether in subclinical HCM (genotype [G]+ left ventricular hypertrophy [LVH]-), ECGI detects early EP abnormality, and in overt HCM, whether the EP substrate relates to genetic status (G+/G-LVH+) and structural phenotype.

METHODS

This was a prospective 211-participant CMR-ECGI multicenter study of 70 G+LVH-, 104 LVH+ (51 G+/53 G-), and 37 healthy volunteers (HVs). Local activation time (AT), corrected repolarization time, corrected activation-recovery interval, spatial gradients (GAT/GRTc), and signal fractionation were derived from 1,000 epicardial sites per participant. Maximal wall thickness and scar burden were derived from CMR. A support vector machine was built to discriminate G+LVH- from HV and low-risk HCM from those with intermediate/high-risk score or nonsustained ventricular tachycardia.

RESULTS

Compared with HV, subclinical HCM showed mean AT prolongation (P = 0.008) even with normal 12-lead electrocardiograms (ECGs) (P = 0.009), and repolarization was more spatially heterogenous (GRTc: P = 0.005) (23% had normal ECGs). Corrected activation-recovery interval was prolonged in overt vs subclinical HCM (P < 0.001). Mean AT was associated with maximal wall thickness; spatial conduction heterogeneity (GAT) and fractionation were associated with scar (all P < 0.05), and G+LVH+ had more fractionation than G-LVH+ (P = 0.002). The support vector machine discriminated subclinical HCM from HV (10-fold cross-validation accuracy 80% [95% CI: 73%-85%]) and identified patients at higher risk of sudden cardiac death (accuracy 82% [95% CI: 78%-86%]).

CONCLUSIONS

In the absence of LVH or 12-lead ECG abnormalities, HCM sarcomere gene mutation carriers express an aberrant EP phenotype detected by ECGI. In overt HCM, abnormalities occur more severely with adverse structural change and positive genetic status.

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

Introduction

Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.

Methods

Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.

Results

Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.

Discussion

The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.

A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals

In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification.

Real-time estimation of EEG-based engagement in different tasks

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Recording physiological and pathological cortical activity and exogenous electric fields using graphene microtransistor arrays in vitro

Graphene-based solution-gated field-effect transistors (gSGFETs) allow the quantification of the brain's full-band signal. Extracellular alternating current (AC) signals include local field potentials (LFP, population activity within a reach of hundreds of micrometers), multiunit activity (MUA), and ultimately single units. Direct current (DC) potentials are slow brain signals with a frequency under 0.1 Hz, and commonly filtered out by conventional AC amplifiers. This component conveys information about what has been referred to as "infraslow" activity. We used gSGFET arrays to record full-band patterns from both physiological and pathological activity generated by the cerebral cortex. To this end, we used an in vitro preparation of cerebral cortex that generates spontaneous rhythmic activity, such as that occurring in slow wave sleep. This examination extended to experimentally induced pathological activities, including epileptiform discharges and cortical spreading depression. Validation of recordings obtained via gSGFETs, including both AC and DC components, was accomplished by cross-referencing with well-established technologies, thereby quantifying these components across different activity patterns. We then explored an additional gSGFET potential application, which is the measure of externally induced electric fields such as those used in therapeutic neuromodulation in humans. Finally, we tested the gSGFETs in human cortical slices obtained intrasurgically. In conclusion, this study offers a comprehensive characterization of gSGFETs for brain recordings, with a focus on potential clinical applications of this emerging technology.

Technical development and feasibility of a reusable vest to integrate cardiovascular magnetic resonance with electrocardiographic imaging

BACKGROUND

Electrocardiographic imaging (ECGI) generates electrophysiological (EP) biomarkers while cardiovascular magnetic resonance (CMR) imaging provides data about myocardial structure, function and tissue substrate. Combining this information in one examination is desirable but requires an affordable, reusable, and high-throughput solution. We therefore developed the CMR-ECGI vest and carried out this technical development study to assess its feasibility and repeatability in vivo.

METHODS

CMR was prospectively performed at 3T on participants after collecting surface potentials using the locally designed and fabricated 256-lead ECGI vest. Epicardial maps were reconstructed to generate local EP parameters such as activation time (AT), repolarization time (RT) and activation recovery intervals (ARI). 20 intra- and inter-observer and 8 scan re-scan repeatability tests.

RESULTS

77 participants were recruited: 27 young healthy volunteers (HV, 38.9 ± 8.5 years, 35% male) and 50 older persons (77.0 ± 0.1 years, 52% male). CMR-ECGI was achieved in all participants using the same reusable, washable vest without complications. Intra- and inter-observer variability was low (correlation coefficients [rs] across unipolar electrograms = 0.99 and 0.98 respectively) and scan re-scan repeatability was high (rs between 0.81 and 0.93). Compared to young HV, older persons had significantly longer RT (296.8 vs 289.3 ms, p = 0.002), ARI (249.8 vs 235.1 ms, p = 0.002) and local gradients of AT, RT and ARI (0.40 vs 0.34 ms/mm, p = 0,01; 0.92 vs 0.77 ms/mm, p = 0.03; and 1.12 vs 0.92 ms/mm, p = 0.01 respectively).

CONCLUSION

Our high-throughput CMR-ECGI solution is feasible and shows good reproducibility in younger and older participants. This new technology is now scalable for high throughput research to provide novel insights into arrhythmogenesis and potentially pave the way for more personalised risk stratification.

CLINICAL TRIAL REGISTRATION

Title: Multimorbidity Life-Course Approach to Myocardial Health-A Cardiac Sub-Study of the MRC National Survey of Health and Development (NSHD) (MyoFit46). National Clinical Trials (NCT) number: NCT05455125. URL: https://clinicaltrials.gov/ct2/show/NCT05455125?term=MyoFit&draw=2&rank=1.

Brain-computer interface treatment for gait rehabilitation in stroke patients

The use of Brain-Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stroke patients with gait disability were recruited for this study. Participants performed 25 sessions with the MI-BCI and assessment visits to track functional changes during the therapy. The results of this study demonstrated a clinically significant increase in walking speed of 0.19 m/s, 95%CI [0.13-0.25], p < 0.001. Patients also reduced spasticity and improved their range of motion and muscle contraction. The BCI treatment was effective in promoting long-lasting functional improvements in the gait speed of chronic stroke survivors. Patients have more movements in the lower limb; therefore, they can walk better and safer. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

Characterization of High-Gamma Activity in Electrocorticographic Signals

Introduction

Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information.

Methods

To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA.

Results

The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks.

Discussion

This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification

Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy.

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study

Objective

Clinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach.

Materials and methods

For 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies.

Results

Over repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments.

Conclusion

Brain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach.

Significance

The integration of different diagnostic methods leads to a better knowledge and care for the DOC.

Optimizing Motor Imagery Parameters for Robotic Arm Control by Brain-Computer Interface

Brain-Computer Interface (BCI) technology has been shown to provide new communication possibilities, conveying brain information externally. BCI-based robot control has started to play an important role, especially in medically assistive robots but not only there. For example, a BCI-controlled robotic arm can provide patients diagnosed with neurodegenerative diseases such as Locked-in syndrome (LIS), Amyotrophic lateral sclerosis (ALS), and others with the ability to manipulate different objects. This study presents the optimization of the configuration parameters of a three-class Motor Imagery (MI) -based BCI for controlling a six Degrees of Freedom (DOF) robotic arm in a plane. Electroencephalography (EEG) signals are recorded from 64 positions on the scalp according to the International 10-10 System. In terms of the resulting classification of error rates, we investigated twelve time windows for the spatial filter and classifier calculation and three time windows for the variance smoothing time. The lowest error rates were achieved when using a 3 s time window for creating the spatial filters and classifier, for a variance time window of 1.5 s.

Study protocol: MyoFit46-the cardiac sub-study of the MRC National Survey of Health and Development

BACKGROUND

The life course accumulation of overt and subclinical myocardial dysfunction contributes to older age mortality, frailty, disability and loss of independence. The Medical Research Council National Survey of Health and Development (NSHD) is the world's longest running continued surveillance birth cohort providing a unique opportunity to understand life course determinants of myocardial dysfunction as part of MyoFit46-the cardiac sub-study of the NSHD.

METHODS

We aim to recruit 550 NSHD participants of approximately 75 years+ to undertake high-density surface electrocardiographic imaging (ECGI) and stress perfusion cardiovascular magnetic resonance (CMR). Through comprehensive myocardial tissue characterization and 4-dimensional flow we hope to better understand the burden of clinical and subclinical cardiovascular disease. Supercomputers will be used to combine the multi-scale ECGI and CMR datasets per participant. Rarely available, prospectively collected whole-of-life data on exposures, traditional risk factors and multimorbidity will be studied to identify risk trajectories, critical change periods, mediators and cumulative impacts on the myocardium.

DISCUSSION

By combining well curated, prospectively acquired longitudinal data of the NSHD with novel CMR-ECGI data and sharing these results and associated pipelines with the CMR community, MyoFit46 seeks to transform our understanding of how early, mid and later-life risk factor trajectories interact to determine the state of cardiovascular health in older age.

TRIAL REGISTRATION

Prospectively registered on ClinicalTrials.gov with trial ID: 19/LO/1774 Multimorbidity Life-Course Approach to Myocardial Health- A Cardiac Sub-Study of the MCRC National Survey of Health and Development (NSHD).

Full-bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene microtransistor depth neural probes

Mapping the entire frequency bandwidth of brain electrophysiological signals is of paramount importance for understanding physiological and pathological states. The ability to record simultaneously DC-shifts, infraslow oscillations (<0.1 Hz), typical local field potentials (0.1-80 Hz) and higher frequencies (80-600 Hz) using the same recording site would particularly benefit preclinical epilepsy research and could provide clinical biomarkers for improved seizure onset zone delineation. However, commonly used metal microelectrode technology suffers from instabilities that hamper the high fidelity of DC-coupled recordings, which are needed to access signals of very low frequency. In this study we used flexible graphene depth neural probes (gDNPs), consisting of a linear array of graphene microtransistors, to concurrently record DC-shifts and high-frequency neuronal activity in awake rodents. We show here that gDNPs can reliably record and map with high spatial resolution seizures, pre-ictal DC-shifts and seizure-associated spreading depolarizations together with higher frequencies through the cortical laminae to the hippocampus in a mouse model of chemically induced seizures. Moreover, we demonstrate the functionality of chronically implanted devices over 10 weeks by recording with high fidelity spontaneous spike-wave discharges and associated infraslow oscillations in a rat model of absence epilepsy. Altogether, our work highlights the suitability of this technology for in vivo electrophysiology research, and in particular epilepsy research, by allowing stable and chronic DC-coupled recordings.

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.

Brain-Computer Interfaces in Acute and Subacute Disorders of Consciousness

SUMMARY

Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients. The application of brain-computer interfaces in disorders of consciousness can be divided into four fields: assessment, communication, prediction, and rehabilitation. The operational theoretical model of consciousness that brain-computer interfaces explore was reviewed in this article, with a focus on studies with acute and subacute patients. We then proposed a clinically friendly guideline, which could contribute to the implementation of brain-computer interfaces in neurorehabilitation settings. Finally, we discussed limitations and future directions, including major challenges and possible solutions.

EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation

Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.

What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?

Description In this review article, we aimed to create a summary of the effects of internal variables on the performance of sensorimotor rhythm-based brain computer interfaces (SMR-BCIs). SMR-BCIs can be potentially used for interfacing between the brain and devices, bypassing usual central nervous system output, such as muscle activity. The careful consideration of internal factors, affecting SMR-BCI performance, can maximize BCI application in both healthy and disabled people. Internal variables may be generalized as descriptors of the processes mainly dependent on the BCI user and/or originating within the user. The current review aimed to critically evaluate and summarize the currently accumulated body of knowledge regarding the effect of internal variables on SMR-BCI performance. The examples of such internal variables include motor imagery, hand coordination, attention, motivation, quality of life, mood and neurophysiological signals other than SMR. We will conclude our review with the discussion about the future developments regarding the research on the effects of internal variables on SMR-BCI performance. The end-goal of this review paper is to provide current BCI users and researchers with the reference guide that can help them optimize the SMR-BCI performance by accounting for possible influences of various internal factors.

What External Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?

Description Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) are used for the acquisition and translation of motor imagery-related brain signals into machine control commands, bypassing the usual central nervous system output. The selection of optimal external variable configuration can maximize SMR-BCI performance in both healthy and disabled people. This performance is especially important now when the BCI is targeted for everyday use in the environment beyond strictly regulated laboratory settings. In this review article, we summarize and critically evaluate the current body of knowledge pertaining to the effect of the external variables on SMR-BCI performance. When assessing the relationship between SMR-BCI performance and external variables, we broadly characterize them as elements that are less dependent on the BCI user and originate from beyond the user. These elements include such factors as BCI type, distractors, training, visual and auditory feedback, virtual reality and magneto electric feedback, proprioceptive and haptic feedback, carefulness of electroencephalography (EEG) system assembling and positioning of EEG electrodes as well as recording-related artifacts. At the end of this review paper, future developments are proposed regarding the research into the effects of external variables on SMR-BCI performance. We believe that our critical review will be of value for academic BCI scientists and developers and clinical professionals working in the field of BCIs as well as for SMR-BCI users.

Evaluating a Novel P300-Based Real-Time Image Ranking BCI

Brain–computer interfaces (BCIs) establish communication between a human brain and a computer or external devices by translating the electroencephalography (EEG) signal into computer commands. After stimulating a sensory organ, a positive deflection of the EEG signal between 250 and 700 ms can be measured. This signal component of the event-related potential (ERP) is called “P300.” Numerous studies have provided evidence that the P300 amplitude and latency are linked to sensory perception, engagement, and cognition. Combining the advances in technology, classification methods, and signal processing, we developed a novel image ranking system called the Unicorn Blondy Check. In this study, the application was tested on 21 subjects using three different visual oddball paradigms. Two consisted of female faces and gray-scale images, while the third test paradigm consisted of familiar and unfamiliar faces. The images were displayed for a duration of 150 ms in a randomized order. The system was trained using 50 trials and tested with 30 trials. The EEG data were acquired using the Unicorn Hybrid Black eight-channel BCI system. These synchronized recordings were analyzed, and the achieved classification accuracies were calculated. The EEG signal was averaged over all participants and for every paradigm separately. Analysis of the EEG data revealed a significant shift in the P300 latency dependent on the paradigm and decreased amplitude for a lower target to non-target ratio. The image ranking application achieved a mean accuracy of 100 and 95.5% for ranking female faces above gray-scale images with ratios of 1:11 and 5:11, respectively. In the case of four familiar faces to 24 unfamiliar faces, 86.4% was reached. The obtained results illustrate this novel system’s functionality due to accuracies above chance levels for all subjects.

Characterization of optogenetically-induced cortical spreading depression in awake mice using graphene micro-transistor arrays

Objective.The development of experimental methodology utilizing graphene micro-transistor arrays to facilitate and advance translational research into cortical spreading depression (CSD) in the awake brain.Approach.CSDs were reliably induced in awake nontransgenic mice using optogenetic methods. High-fidelity DC-coupled electrophysiological mapping of propagating CSDs was obtained using flexible arrays of graphene soultion-gated field-effect transistors (gSGFETs).Main results.Viral vectors targetted channelrhopsin expression in neurons of the motor cortex resulting in a transduction volume ⩾1 mm³. 5-10 s of continous blue light stimulation induced CSD that propagated across the cortex at a velocity of 3.0 ± 0.1 mm min^-1. Graphene micro-transistor arrays enabled high-density mapping of infraslow activity correlated with neuronal activity suppression across multiple frequency bands during both CSD initiation and propagation. Localized differences in the CSD waveform could be detected and categorized into distinct clusters demonstrating the spatial resolution advantages of DC-coupled recordings. We exploited the reliable and repeatable induction of CSDs using this preparation to perform proof-of-principle pharmacological interrogation studies using NMDA antagonists. MK801 (3 mg kg^-1) suppressed CSD induction and propagation, an effect mirrored, albeit transiently, by ketamine (15 mg kg^-1), thus demonstrating this models' applicability as a preclinical drug screening platform. Finally, we report that CSDs could be detected through the skull using graphene micro-transistors, highlighting additional advantages and future applications of this technology.Significance.CSD is thought to contribute to the pathophysiology of several neurological diseases. CSD research will benefit from technological advances that permit high density electrophysiological mapping of the CSD waveform and propagation across the cortex. We report anin vivoassay that permits minimally invasive optogenetic induction, combined with multichannel DC-coupled recordings enabled by gSGFETs in the awake brain. Adoption of this technological approach could facilitate and transform preclinical investigations of CSD in disease relevant models.

Multi-modal Mapping of the Face Selective Ventral Temporal Cortex-A Group Study With Clinical Implications for ECS, ECoG, and fMRI

Face recognition is impaired in patients with prosopagnosia, which may occur as a side effect of neurosurgical procedures. Face selective regions on the ventral temporal cortex have been localized with electrical cortical stimulation (ECS), electrocorticography (ECoG), and functional magnetic resonance imagining (fMRI). This is the first group study using within-patient comparisons to validate face selective regions mapping, utilizing the aforementioned modalities. Five patients underwent surgical treatment of intractable epilepsy and joined the study. Subdural grid electrodes were implanted on their ventral temporal cortices to localize seizure foci and face selective regions as part of the functional mapping protocol. Face selective regions were identified in all patients with fMRI, four patients with ECoG, and two patients with ECS. From 177 tested electrode locations in the region of interest (ROI), which is defined by the fusiform gyrus and the inferior temporal gyrus, 54 face locations were identified by at least one modality in all patients. fMRI mapping showed the highest detection rate, revealing 70.4% for face selective locations, whereas ECoG and ECS identified 64.8 and 31.5%, respectively. Thus, 28 face locations were co-localized by at least two modalities, with detection rates of 89.3% for fMRI, 85.7% for ECoG and 53.6 % for ECS. All five patients had no face recognition deficits after surgery, even though five of the face selective locations, one obtained by ECoG and the other four by fMRI, were within 10 mm to the resected volumes. Moreover, fMRI included a quite large volume artifact on the ventral temporal cortex in the ROI from the anatomical structures of the temporal base. In conclusion, ECS was not sensitive in several patients, whereas ECoG and fMRI even showed activation within 10 mm to the resected volumes. Considering the potential signal drop-out in fMRI makes ECoG the most reliable tool to identify face selective locations in this study. A multimodal approach can improve the specificity of ECoG and fMRI, while simultaneously minimizing the number of required ECS sessions. Hence, all modalities should be considered in a clinical mapping protocol entailing combined results of co-localized face selective locations.

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients-A Feasibility Study

Introduction

Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks.

Methods

Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy.

Results

The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), ΔFMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), ΔMAS-wrist = -0.72 points (SD = 0.83), P < 0.001, ΔMAS-fingers = -0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47–6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage.

Conclusion

The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

Auditory and Somatosensory P3 Are Complementary for the Assessment of Patients with Disorders of Consciousness

The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awareness. Some patients, although with remaining cognitive abilities, are not able to exhibit overt voluntary responses at the bedside and may be misdiagnosed as UWS. Several studies investigated functional neuroimaging and neurophysiology as an additional tool to evaluate the level of consciousness and to detect covert command following in DOC. Most of these studies are based on auditory stimulation, neglecting patients suffering from decreased or absent hearing abilities. In the present study, we aim to assess the response to a P3-based paradigm in 40 patients with DOC and 12 healthy participants using auditory (AEP) and vibrotactile (VTP) stimulation. To this end, an EEG-based brain-computer interface was used at DOC patient's bedside. We compared the significance of the P3 performance (i.e., the interpretation of significance of the evoked P3 response) as obtained by 'direct processing' (i.e., theoretical-based significance threshold) and 'offline processing' (i.e., permutation-based single subject level threshold). We evaluated whether the P3 performances were dependent on clinical variables such as diagnosis (UWS and MCS), aetiology and time since injury. Last we tested the dependency of AEP and VTP performances at the single subject level. Direct processing tends to overestimate P3 performance. We did not find any difference in the presence of a P3 performance according to the level of consciousness (UWS vs. MCS) or the aetiology (traumatic vs. non-traumatic brain injury). The performance achieved at the AEP paradigm was independent from what was achieved at the VTP paradigm, indicating that some patients performed better on the AEP task while others performed better on the VTP task. Our results support the importance of using multimodal approaches in the assessment of DOC patients in order to optimise the evaluation of patient's abilities.

Effects of Gamification in BCI Functional Rehabilitation

Objective

To evaluate whether introducing gamification in BCI rehabilitation of the upper limbs of post-stroke patients has a positive impact on their experience without altering their efficacy in creating motor mental images (MI).

Design

A game was designed purposely adapted to the pace and goals of an established BCI-rehabilitation protocol. Rehabilitation was based on a double feedback: functional electrostimulation and animation of a virtual avatar of the patient’s limbs. The game introduced a narrative on top of this visual feedback with an external goal to achieve (protecting bits of cheese from a rat character). A pilot study was performed with 10 patients and a control group of six volunteers. Two rehabilitation sessions were done, each made up of one stage of calibration and two training stages, some stages with the game and others without. The accuracy of the classification computed was taken as a measure to compare the efficacy of MI. Users’ opinions were gathered through a questionnaire. No potentially identifiable human images or data are presented in this study.

Results

The gamified rehabilitation presented in the pilot study does not impact on the efficacy of MI, but it improves users experience making it more fun.

Conclusion

These preliminary results are encouraging to continue investigating how game narratives can be introduced in BCI rehabilitation to make it more gratifying and engaging.

EEG Biomarkers Related With the Functional State of Stroke Patients

Introduction

Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment.

Methods

Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps.

Results

The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median [IQR: 0–8], P = 0.002).

Conclusion

The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.

Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients' cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8-12 runs each day. Vibrotactile tactors were placed on the each patient's left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (p = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.

Tailor-Made Surgery Based on Functional Networks for Intractable Epilepsy

Normal and pathological networks related to seizure propagation have got attention to elucide complex seizure semiology and contribute to diagnosis and surgical monitoring in epilepsy treatment. Since focal and generalized epileptogenic syndromes abnormalities might involve multiple foci and large-scale networks, we applied electrophysiolpgy (cortco-cortico evoked potential; CCEP), and tractography to make detailed diagnosis for complex syndrome. All 14 epilepsy patients with no or little abnormality on images investigations underwent subdural grid implantation for epilepsy diagnosis. To perform quick network analysis, we recorded and analyzed high gamma activity (HGA) of epileptogenic activity and CCEPs to identify pathological activity distribution and network connectivity. [Results] Pathological CCEPs showed two negative deflections consisting of early (>40 ms) and late (>150 ms) components in electrically stable circumstance at bed side and early CCEPs appeared in 57% of the patients. On the basis of the CCEP findings, tractography detected anatomical connections. Early components of pathological CCEPs diminished after complete disconnection of tractoography-based fibers between the foci in seven of eight cases. One case with residual pathological CCEPs showed poorer outcome. Thirteen (92.8%) patients with or without CCEPs who underwent network surgery had favorable prognosis except for a case with wide traumatic epilepsy. Intraoperative CCEP measurements and HGA mapping enabled visualization of pathological networks and clinical impotence as a biomarker to improve functional prognosis. HGA/CCEP recording should shed light on pathological and complex propagation for epilepsy surgery.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Disconnection of the pathological connectome for multifocal epilepsy surgery

OBJECTIVERecent neuroimaging studies suggest that intractable epilepsy involves pathological functional networks as well as strong epileptogenic foci. Combining cortico-cortical evoked potential (CCEP) recording and tractography is a useful strategy for mapping functional connectivity in normal and pathological networks. In this study, the authors sought to demonstrate the efficacy of preoperative combined CCEP recording, high gamma activity (HGA) mapping, and tractography for surgical planning, and of intraoperative CCEP measures for confirmation of selective pathological network disconnection.METHODSThe authors treated 4 cases of intractable epilepsy. Diffusion tensor imaging-based tractography data were acquired before the first surgery for subdural grid implantation. HGA and CCEP investigations were done after the first surgery, before the second surgery was performed to resect epileptogenic foci, with continuous CCEP monitoring during resection.RESULTSAll 4 patients in this report had measurable pathological CCEPs. The mean negative peak-1 latency of normal CCEPs related to language functions was 22.2 ± 3.5 msec, whereas pathological CCEP latencies varied between 18.1 and 22.4 msec. Pathological CCEPs diminished after complete disconnection in all cases. At last follow-up, all of the patients were in long-term postoperative seizure-free status, although 1 patient still suffered from visual aura every other month.CONCLUSIONSCombined CCEP measurement, HGA mapping, and tractography greatly facilitated targeted disconnection of pathological networks in this study. Although CCEP recording requires technical expertise, it allows for assessment of pathological network involvement in intractable epilepsy and may improve seizure outcome.

Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces

Invasive brain-computer interfaces yield remarkable performance in a multitude of applications. For classification experiments, high-gamma bandpower features and linear discriminant analysis (LDA) are commonly used due to simplicity and robustness. However, LDA is inherently static and not suited to account for transient information that is typically present in high-gamma features. To resolve this issue, we here present an extension of LDA to the time-variant feature space. We call this method time-variant linear discriminant analysis (TVLDA). It intrinsically provides a feature reduction stage, which makes external approaches thereto obsolete, such as feature selection techniques or common spatial patterns (CSPs). As well, we propose a time-domain whitening stage which equalizes the pronounced 1/f-shape of the typical brain-wave spectrum. We evaluated our proposed architecture based on recordings from 15 epilepsy patients with temporarily implanted subdural grids, who participated in additional research experiments besides clinical treatment. The experiments featured two different motor tasks involving three high-level gestures and individual finger movement. We used log-transformed bandpower features from the high-gamma band (50-300 Hz, excluding power-line harmonics) for classification. On average, whitening improved the classification performance by about 11%. On whitened data, TVLDA outperformed LDA with feature selection by 11.8%, LDA with CSPs by 13.9%, and regularized LDA with vectorized features by 16.4%. At the same time, TVLDA only required one or two internal features to achieve this. TVLDA provides stable results even if very few trials are available. It is easy to implement, fully automatic and deterministic. Due to its low complexity, TVLDA is suited for real-time brain-computer interfaces. Training is done in less than a second. TVLDA performed particularly well in experiments with data from high-density electrode arrays. For example, the three high-level gestures were correctly identified at a rate of 99% over all subjects. Similarly, the decoding accuracy of individual fingers was 96% on average over all subjects. To our knowledge, these mean accuracies are the highest ever reported for three-class and five-class motor-control BCIs.

Multispectrum Indocyanine Green Videography for Visualizing Brain Vascular Pathology

OBJECTIVE

Currently, neurosurgical vascular surgery frequently uses indocyanine green (ICG)-videography (VG) to evaluate the blood flow in brain vessels. Although ICG-VG delineates intravascular ICG fluorescence as a high-intensity signal in gray-scale with dark background, it is hard to identify anatomical structures, including vasculature or surgical devices simultaneously. This report developed combination of a near-infrared (NIR) camera with particular sensitivity and an optical filter to observe the blood-flow conditions and anatomical structures.

METHODS

To overcome the specific issues of ICG-VG, we applied a high-sensitivity camera with a 980-nm NIR component to delineate anatomical and fluorescence images, detecting signals between 830 and 1000 nm simultaneously during operation. We used a diluted ICG phantom to evaluate fluorescence signal changes by changing wavelength of the emission light. For clinical use, we used a high-sensitivity NIR camera with a high-pass filter on a surgical microscope. The new NIR system detected signals between 770 and 1000 nm, and the lighting system illuminated objects mainly at 980-nm wavelength. Both images with the blood flow and anatomical structures were projected to the smart glasses in real time.

RESULTS

In the phantom experiment, we found that the emission light with wide band widths (575-800 nm) evoked various intensities of ICG fluorescence. This new NIR system allowed us to observe ICG fluorescence and anatomical structures without image fusion or time-delay. The both information of anatomy and fluorescence was projected on wearable smart glasses. Furthermore, the new NIR system detected ICG-fluorescence signals for a longer duration than the original camera, which allowed us to achieve careful and detailed observation of more vasculature and fine vessels.

CONCLUSIONS

This study proposes a new NIR system and emphasizes simultaneous observation of anatomy and fluorescence signals during operation. It paves the way for further possibilities in the development of optical systems. To understand the natural phenomena and combination of different scientific and clinical fields, it might be important to understand and combine not only fluorescence, but also natural science, optics, and background pathology. This simple system would be available for neuroendoscope and robotic surgery.

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10–24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.

Passive functional mapping of receptive language areas using electrocorticographic signals

OBJECTIVE

To validate the use of passive functional mapping using electrocorticographic (ECoG) broadband gamma signals for identifying receptive language cortex.

METHODS

We mapped language function in 23 patients using ECoG and using electrical cortical stimulation (ECS) in a subset of 15 subjects.

RESULTS

The qualitative comparison between cortical sites identified by ECoG and ECS show a high concordance. A quantitative comparison indicates a high level of sensitivity (95%) and a lower level of specificity (59%). Detailed analysis reveals that 82% of all cortical sites identified by ECoG were within one contact of a site identified by ECS.

CONCLUSIONS

These results show that passive functional mapping reliably localizes receptive language areas, and that there is a substantial concordance between the ECoG- and ECS-based methods. They also point to a more refined understanding of the differences between ECoG- and ECS-based mappings. This refined understanding helps to clarify the instances in which the two methods disagree and can explain why neurosurgical practice has established the concept of a "safety margin."

SIGNIFICANCE

Passive functional mapping using ECoG signals provides a fast, robust, and reliable method for identifying receptive language areas without many of the risks and limitations associated with ECS.

Performance Differences Using a Vibro-Tactile P300 BCI in LIS-Patients Diagnosed With Stroke and ALS

Patients with locked-in syndrome (LIS) are typically unable to move or communicate and can be misdiagnosed as patients with disorders of consciousness (DOC). Behavioral assessment scales are limited in their ability to detect signs of consciousness in this population. Recent research has shown that brain-computer interface (BCI) technology could supplement behavioral scales and allows to establish communication with these severely disabled patients. In this study, we compared the vibro-tactile P300 based BCI performance in two groups of patients with LIS of different etiologies: stroke (n = 6) and amyotrophic lateral sclerosis (ALS) (n = 9). Two vibro-tactile paradigms were administered to the patients to assess conscious function and command following. The first paradigm is called vibrotactile evoked potentials (EPs) with two tactors (VT2), where two stimulators were placed on the patient’s left and right wrist, respectively. The patients were asked to count the rare stimuli presented to one wrist to elicit a P300 complex to target stimuli only. In the second paradigm, namely vibrotactile EPs with three tactors (VT3), two stimulators were placed on the wrists as done in VT2, and one additional stimulator was placed on his/her back. The task was to count the rare stimuli presented to one wrist, to elicit the event-related potentials (ERPs). The VT3 paradigm could also be used for communication. For this purpose, the patient had to count the stimuli presented to the left hand to answer “yes” and to count the stimuli presented to the right hand to answer “no.” All patients except one performed above chance level in at least one run in the VT2 paradigm. In the VT3 paradigm, all 6 stroke patients and 8/9 ALS patients showed at least one run above chance. Overall, patients achieved higher accuracies in VT2 than VT3. LIS patients due to ALS exhibited higher accuracies that LIS patients due to stroke, in both the VT2 and VT3 paradigms. These initial data suggest that controlling this type of BCI requires specific cognitive abilities that may be impaired in certain sub-groups of severely motor-impaired patients. Future studies on a larger cohort of patients are needed to better identify and understand the underlying cortical mechanisms of these differences.

Assessing Command-Following and Communication With Vibro-Tactile P300 Brain-Computer Interface Tools in Patients With Unresponsive Wakefulness Syndrome

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor disablities, and thus assessing their spared cognitive abilities can be difficult. Recent research from several groups has shown that non-invasive brain-computer interface (BCI) technology can provide assessments of these patients' cognitive function that can supplement information provided through conventional behavioral assessment methods. In rare cases, BCIs may provide a binary communication mechanism. Here, we present results from a vibrotactile BCI assessment aiming at detecting command-following and communication in 12 unresponsive wakefulness syndrome (UWS) patients. Two different paradigms were administered at least once for every patient: (i) VT2 with two vibro-tactile stimulators fixed on the patient's left and right wrists and (ii) VT3 with three vibro-tactile stimulators fixed on both wrists and on the back. The patients were instructed to mentally count either the stimuli on the left or right wrist, which may elicit a robust P300 for the target wrist only. The EEG data from −100 to +600 ms around each stimulus were extracted and sub-divided into 8 data segments. This data was classified with linear discriminant analysis (using a 10 × 10 cross validation) and used to calibrate a BCI to assess command following and YES/NO communication abilities. The grand average VT2 accuracy across all patients was 38.3%, and the VT3 accuracy was 26.3%. Two patients achieved VT3 accuracy ≥80% and went through communication testing. One of these patients answered 4 out of 5 questions correctly in session 1, whereas the other patient answered 6/10 and 7/10 questions correctly in sessions 2 and 4. In 6 other patients, the VT2 or VT3 accuracy was above the significance threshold of 23% for at least one run, while in 4 patients, the accuracy was always below this threshold. The study highlights the importance of repeating EEG assessments to increase the chance of detecting command-following in patients with severe brain injury. Furthermore, the study shows that BCI technology can test command following in chronic UWS patients and can allow some of these patients to answer YES/NO questions.

BCI Performance and Brain Metabolism Profile in Severely Brain-Injured Patients Without Response to Command at Bedside

Detection and interpretation of signs of “covert command following” in patients with disorders of consciousness (DOC) remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish “covert command following.” These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed “covert command following” (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without “covert command-following” but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe “covert command following” in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with DOC.