The correlation between motor impairments and event-related desynchronization during motor imagery in ALS patients

Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

Background

As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited.

Methods

To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features.

Results

Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p < 0.05).

Conclusion

It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.

Evaluating the perspectives of those with severe physical impairments while learning BCI control of a commercial augmentative and alternative communication paradigm

Augmentative and alternative communication (AAC) techniques can provide access to communication for individuals with severe physical impairments. Brain-computer interface (BCI) access techniques may serve alongside existing AAC access methods to provide communication device control. However, there is limited information available about how individual perspectives change with motor-based BCI-AAC learning. Four individuals with ALS completed 12 BCI-AAC training sessions in which they made letter selections during an automatic row-column scanning pattern via a motor-based BCI-AAC. Recurring measures were taken before and after each BCI-AAC training session to evaluate changes associated with BCI-AAC performance, and included measures of fatigue, frustration, mental effort, physical effort, device satisfaction, and overall ease of device control. Levels of pre- to post-fatigue were low for use of the BCI-AAC system. However, participants indicated different perceptions of the term fatigue, with three participants discussing fatigue to be generally synonymous with physical effort, and one mental effort. Satisfaction with the BCI-AAC system was related to BCI-AAC performance for two participants, and levels of frustration for two participants. Considering a range of person-centered measures in future clinical BCI-AAC applications is important for optimizing and standardizing BCI-AAC assessment procedures.

Movement-related beta ERD and ERS abnormalities in neuropsychiatric disorders

Movement-related oscillations in the beta range (from 13 to 30 Hz) have been observed over sensorimotor areas with power decrease (i.e., event-related desynchronization, ERD) during motor planning and execution followed by an increase (i.e., event-related synchronization, ERS) after the movement's end. These phenomena occur during active, passive, imaged, and observed movements. Several electrophysiology studies have used beta ERD and ERS as functional indices of sensorimotor integrity, primarily in diseases affecting the motor system. Recent literature also highlights other characteristics of beta ERD and ERS, implying their role in processes not strictly related to motor function. Here we review studies about movement-related ERD and ERS in diseases characterized by motor dysfunction, including Parkinson's disease, dystonia, stroke, amyotrophic lateral sclerosis, cerebral palsy, and multiple sclerosis. We also review changes of beta ERD and ERS reported in physiological aging, Alzheimer's disease, and schizophrenia, three conditions without overt motor symptoms. The review of these works shows that ERD and ERS abnormalities are present across the spectrum of the examined pathologies as well as development and aging. They further suggest that cognition and movement are tightly related processes that may share common mechanisms regulated by beta modulation. Future studies with a multimodal approach are warranted to understand not only the specific topographical dynamics of movement-related beta modulation but also the general meaning of beta frequency changes occurring in relation to movement and cognitive processes at large. Such an approach will provide the foundation to devise and implement novel therapeutic approaches to neuropsychiatric disorders.

A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI

Decoding neural responses from multimodal information sources, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has the transformative potential to advance hybrid brain-computer interfaces (hBCIs). However, existing modest performance improvement of hBCIs might be attributed to the lack of computational frameworks that exploit complementary synergistic properties in multimodal features. This study proposes a multimodal data fusion framework to represent and decode synergistic multimodal motor imagery (MI) neural responses. We hypothesize that exploiting EEG nonlinear dynamics adds a new informative dimension to the commonly combined EEG-fNIRS features and will ultimately increase the synergy between EEG and fNIRS features toward an enhanced hBCI. The EEG nonlinear dynamics were quantified by extracting graph-based recurrence quantification analysis (RQA) features to complement the commonly used spectral features for an enhanced multimodal configuration when combined with fNIRS. The high-dimensional multimodal features were further given to a feature selection algorithm relying on the least absolute shrinkage and selection operator (LASSO) for fused feature selection. Linear support vector machine (SVM) was then used to evaluate the framework. The mean hybrid classification performance improved by up to 15% and 4% compared to the unimodal EEG and fNIRS, respectively. The proposed graph-based framework substantially increased the contribution of EEG features for hBCI classification from 28.16% up to 52.9% when introduced the nonlinear dynamics and improved the performance by approximately 2%. These findings suggest that graph-based nonlinear dynamics can increase the synergy between EEG and fNIRS features for an enhanced MI response representation that is not dominated by a single modality.

Cortical Hyperexcitability in the Driver’s Seat in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal disease characterized by the degeneration of cortical and spinal motor neurons. With no effective treatment available to date, patients face progressive paralysis and eventually succumb to the disease due to respiratory failure within only a few years. Recent research has revealed the multifaceted nature of the mechanisms and cell types involved in motor neuron degeneration, thereby opening up new therapeutic avenues. Intriguingly, two key features present in both ALS patients and rodent models of the disease are cortical hyperexcitability and hyperconnectivity, the mechanisms of which are still not fully understood. We here recapitulate current findings arguing for cell autonomous and non-cell autonomous mechanisms causing cortical excitation and inhibition imbalance, which is involved in the degeneration of motor neurons in ALS. Moreover, we will highlight recent evidence that strongly indicates a cardinal role for the motor cortex as a main driver and source of the disease, thus arguing for a corticofugal trajectory of the pathology.

Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs

Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI.

METHOD

EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier.

RESULTS

Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features.

SIGNIFICANCE

These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.

Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm: An ECoG case study

Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis

We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS.

Evaluating person-centered factors associated with brain-computer interface access to a commercial augmentative and alternative communication paradigm

Current BCI-AAC systems largely utilize custom-made software and displays that may be unfamiliar to AAC stakeholders. Further, there is limited information available exploring the heterogenous profiles of individuals who may use BCI-AAC. Therefore, in this study, we aimed to evaluate how individuals with amyotrophic lateral sclerosis (ALS) learned to control a motor-based BCI switch in a row-column AAC scanning pattern, and person-centered factors associated with BCI-AAC performance. Four individuals with ALS completed 12 BCI-AAC training sessions, and three individuals without neurological impairment completed 3 BCI-AAC training sessions. To assess person-centered factors associated with BCI-AAC performance, participants completed both initial and recurring assessment measures including levels of cognition, motor ability, fatigue, and motivation. Three of four participants demonstrated either BCI-AAC performance in the range of neurotypical peers, or an improving BCI-AAC learning trajectory. However, BCI-AAC learning trajectories were variable. Assessment measures revealed that two participants presented with a suspicion for cognitive impairment yet achieved the highest levels of BCI-AAC accuracy with their increased levels of performance being possibly supported by largely unimpaired motor skills. Motor-based BCI switch access to a commercial AAC row-column scanning may be feasible for individuals with ALS and possibly supported by timely intervention.

Sustained attention to response task-related beta oscillations relate to performance and provide a functional biomarker in ALS

OBJECTIVE

To characterize the cortical oscillations associated with performance of the sustained attention to response task (SART) and their disruptions in the neurodegenerative condition amyotrophic lateral sclerosis (ALS).

APPROACH

A randomised SART was undertaken by 24 ALS patients and 33 healthy controls during 128-channel electroencephalography. Complex Morlet wavelet transform was used to quantify non-phase-locked oscillatory activity in event-related spectral perturbations associated with performing the SART. We investigated the relationships between these perturbations and task performance, and associated motor and cognitive changes in ALS Main results: SART induced theta-band event-related synchronization (ERS) and alpha- and beta-band event-related desynchronization (ERD), followed by rebound beta ERS, in both Go and NoGo trials across the frontoparietal axis, with NoGo trials eliciting greater theta ERS and lesser beta ERS. Controls with greater Go trial beta ERS performed with greater speed and less accuracy. ALS patients exhibited increased anticipation compared to controls but similar reaction times and accuracy. Prefrontal (AUROC=0.8, Cohen's d=0.97) and parietal (AUROC=0.82, Cohen's d=1.12) beta-band ERD was significantly reduced in ALS but did not relate to performance, while patients with higher ECAS ALS-specific scores demonstrated greater ERS in beta (rho=0.72) upon successful withholding.

SIGNIFICANCE

EEG measurement of task-related oscillation changes reveals variation in cortical network engagement in relation to speed versus accuracy strategies. Such measures can also capture cognitive and motor network pathophysiology in the absence of task performance decline, which may facilitate development of more sensitive early neurodegenerative disease biomarkers.

Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS' brain network and could hypothetically extend to applications in other neurodegenerative diseases.

An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach

OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) has recently gained momentum in research on motor-imagery (MI)-based brain-computer interfaces (BCIs). However, strikingly, most of the research effort is primarily devoted to enhancing fNIRS-based BCIs for healthy individuals. The ability of patients with amyotrophic lateral sclerosis (ALS), among the main BCI end-users to utilize fNIRS-based hemodynamic responses to efficiently control an MI-based BCI, has not yet been explored. This study aims to quantify subject-specific spatio-temporal characteristics of ALS patients' hemodynamic responses to MI tasks, and to investigate the feasibility of using these responses as a means of communication to control a binary BCI.

METHODS

Hemodynamic responses were recorded using fNIRS from eight patients with ALS while performing MI-Rest tasks. The generalized linear model (GLM) analysis was conducted to statistically estimate and evaluate individualized spatial activation. Selected channel sets were statistically optimized for classification. Subject-specific discriminative features, including a proposed data-driven estimated coefficient obtained from GLM, and optimized classification parameters were identified and used to further evaluate the performance using a linear support vector machine (SVM) classifier.

RESULTS

Inter-subject variations were observed in spatio-temporal characteristics of patients' hemodynamic responses. Using optimized classification parameters and feature sets, all subjects could successfully use their MI hemodynamic responses to control a BCI with an average classification accuracy of 85.4% ± 9.8%.

SIGNIFICANCE

Our results indicate a promising application of fNIRS-based MI hemodynamic responses to control a binary BCI by ALS patients. These findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.

An exploration of neural dynamics of motor imagery for people with amyotrophic lateral sclerosis

OBJECTIVE

Studies of the neuropathological effects of amyotrophic lateral sclerosis (ALS) on the underlying motor system have investigated abnormalities in the magnitude and timing of the event-related desynchronization (ERD) and synchronization (ERS) during motor execution (ME). However, the spatio-spectral-temporal dynamics of these sensorimotor oscillations during motor imagery (MI) have not been fully explored for these patients. This study explores the neural dynamics of sensorimotor oscillations for ALS patients during MI by quantifying ERD/ERS features in frequency, time, and space.

APPROACH

Electroencephalogram (EEG) data were recorded from six patients with ALS and 11 age-matched healthy controls (HC) while performing a MI task. ERD/ERS features were extracted using wavelet-based time-frequency analysis and compared between the two groups to quantify the abnormal neural dynamics of ALS in terms of both time and frequency. Topographic correlation analysis was conducted to compare the localization of MI activity between groups and to identify subject-specific frequencies in the µ and β frequency bands.

MAIN RESULTS

Overall, reduced and delayed ERD was observed for ALS patients, particularly during right-hand MI. ERD features were also correlated with ALS clinical scores, specifically disease duration, bulbar, and cognitive functions.

SIGNIFICANCE

The analyses in this study quantify abnormalities in the magnitude and timing of sensorimotor oscillations for ALS patients during MI tasks. Our findings reveal notable differences between MI and existing results on ME in ALS. The observed alterations are speculated to reflect disruptions in the underlying cortical networks involved in MI functions. Quantifying the neural dynamics of MI plays an important role in the study of EEG-based cortical markers for ALS.

Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Purpose

Brain-computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand how electroencephalography (EEG) records the BCI related brain signals, what brain signals are recorded by EEG, and why these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive and motor processes, which are of interest to a range of related disciplines including speech science.

Method

This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC related signals of interest to speech-language-hearing.

Results

An overview of BCI-AAC related signals are provided discussing 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) why these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background.

Conclusion

Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG

This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. In the first phase, EEG signal is segmented into overlapping time segments and bandpass filtered through frequency filter bank of variable size subbands. In the next phase, features are extracted from the segmented and filtered data using stationary common spatial pattern technique (SCSP) that can handle the non- stationarity and artifacts of EEG signal. The univariate feature selection method is used to obtain a relevant subset of features in the third phase. In the final phase, the classifier is used to build adecision model. In this paper, four univariate feature selection methods such as Euclidean distance, correlation, mutual information and Fisher discriminant ratio and two well-known classifiers (LDA and SVM) are investigated. The proposed method has been validated using the publicly available BCI competition IV dataset Ia and BCI Competition III dataset IVa. Experimental results demonstrate that the proposed method significantly outperforms the existing methods in terms of classification error. A reduction of 76.98%, 75.65%, 73.90% and 72.21% in classification error over both datasets and both classifiers can be observed using the proposed CVSTSCSP method in comparison to CSP, SBCSP, FBCSP and CVSCSP respectively.

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.

Oscillatory Activities in Neurological Disorders of Elderly: Biomarkers to Target for Neuromodulation

Non-invasive brain stimulation (NIBS) has been under investigation as adjunct treatment of various neurological disorders with variable success. One challenge is the limited knowledge on what would be effective neuronal targets for an intervention, combined with limited knowledge on the neuronal mechanisms of NIBS. Motivated on the one hand by recent evidence that oscillatory activities in neural systems play a role in orchestrating brain functions and dysfunctions, in particular those of neurological disorders specific of elderly patients, and on the other hand that NIBS techniques may be used to interact with these brain oscillations in a controlled way, we here explore the potential of modulating brain oscillations as an effective strategy for clinical NIBS interventions. We first review the evidence for abnormal oscillatory profiles to be associated with a range of neurological disorders of elderly (e.g., Parkinson's disease (PD), Alzheimer's disease (AD), stroke, epilepsy), and for these signals of abnormal network activity to normalize with treatment, and/or to be predictive of disease progression or recovery. We then ask the question to what extent existing NIBS protocols have been tailored to interact with these oscillations and possibly associated dysfunctions. Our review shows that, despite evidence for both reliable neurophysiological markers of specific oscillatory dis-functionalities in neurological disorders and NIBS protocols potentially able to interact with them, there are few applications of NIBS aiming to explore clinical outcomes of this interaction. Our review article aims to point out oscillatory markers of neurological, which are also suitable targets for modification by NIBS, in order to facilitate in future studies the matching of technical application to clinical targets.

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganization is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

Altered cortical beta-band oscillations reflect motor system degeneration in amyotrophic lateral sclerosis

Continuous rhythmic neuronal oscillations underpin local and regional cortical communication. The impact of the motor system neurodegenerative syndrome amyotrophic lateral sclerosis (ALS) on the neuronal oscillations subserving movement might therefore serve as a sensitive marker of disease activity. Movement preparation and execution are consistently associated with modulations to neuronal oscillation beta (15–30 Hz) power. Cortical beta‐band oscillations were measured using magnetoencephalography (MEG) during preparation for, execution, and completion of a visually cued, lateralized motor task that included movement inhibition trials. Eleven “classical” ALS patients, 9 with the primary lateral sclerosis (PLS) phenotype, and 12 asymptomatic carriers of ALS‐associated gene mutations were compared with age‐similar healthy control groups. Augmented beta desynchronization was observed in both contra‐ and ipsilateral motor cortices of ALS patients during motor preparation. Movement execution coincided with excess beta desynchronization in asymptomatic mutation carriers. Movement completion was followed by a slowed rebound of beta power in all symptomatic patients, further reflected in delayed hemispheric lateralization for beta rebound in the PLS group. This may correspond to the particular involvement of interhemispheric fibers of the corpus callosum previously demonstrated in diffusion tensor imaging studies. We conclude that the ALS spectrum is characterized by intensified cortical beta desynchronization followed by delayed rebound, concordant with a broader concept of cortical hyperexcitability, possibly through loss of inhibitory interneuronal influences. MEG may potentially detect cortical dysfunction prior to the development of overt symptoms, and thus be able to contribute to the assessment of future neuroprotective strategies. Hum Brain Mapp 38:237–254, 2017. © 2016 Wiley Periodicals, Inc.

Brain-computer interfaces for patients with disorders of consciousness

The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.

Decreased movement-related beta desynchronization and impaired post-movement beta rebound in amyotrophic lateral sclerosis

OBJECTIVE

This study explored event-related desynchronization (ERD) and synchronization (ERS) in amyotrophic lateral sclerosis (ALS) to quantify cortical sensorimotor processes during volitional movements. We furthermore compared ERD/ERS measures with clinical scores and movement-related cortical potential (MRCP) amplitudes.

METHODS

Electroencephalograms were recorded while 21 ALS patients and 19 controls performed two self-paced motor tasks: sniffing and right index finger flexion. Based on Wavelet analysis the alpha and beta frequency bands were selected for subsequent evaluation.

RESULTS

Patients generated significantly smaller resting alpha spectral power density (SPD) and smaller beta ERD compared to controls. Additionally patients exhibited merely unilateral post-movement ERS (beta rebound) whereas this phenomenon was bilateral in controls. ERD/ERS amplitudes did not correlate with corresponding MRCPs for either patients or controls.

CONCLUSIONS

The smaller resting alpha SPD and beta ERD and asymmetrical appearance of beta ERS in patients compared to controls could be the result of pyramidal cell degeneration and/or corpus callosum involvement in ALS.

SIGNIFICANCE

These results support the notion of reduced movement preparation in ALS involving also areas outside the motor cortex. Furthermore post-movement cortical inhibition seems to be impaired in ALS. ERD/ERS and MRCP are found to be independent measures of cortical motor functions in ALS.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.