Event-related Desynchronization/synchronization Research Articles

Neural Mechanisms of Visual-Spatial Judgment Behavior under Visual and Auditory Constraints: Evidence from an Electroencephalograph during Handgun Shooting.

Light and noise are important factors affecting shooting performance, and shooters can exhibit physiological processes that differ from normal shooting when they are subjected to disturbed visual and auditory conditions. The purpose of this study was to explore the neural mechanism of shooting preparation in skilled shooters with visual and auditory limitations. We designed an experiment and recorded the electroencephalograph (EEG) and shooting performance indexes of 40 individuals skilled in marksmanship during the shooting preparation stage under three conditions: low light, noise interference, and a normal environment. EEG relative band power features and event-related desynchronization/synchronization (ERD/ERS) features were extracted and analyzed. The results showed that (1) the average score of the shooters was 8.55 under normal conditions, 7.71 under visually restricted conditions, and 8.50 under auditorily restricted conditions; (2) the relative EEG band power in the frontal lobe (Fp1, Fp2), frontal lobe (F4, F8), left temporal region (T7), central lobe (CP2), and parietal lobe (P3, PO3) in the theta band was significantly lower than in the other two environments (p < 0.05), and there was no significant difference between the power intensity of the shooter in the noisy environment and that in the normal environment; and (3) in the low-light environment, a significant negative correlation was found between the central region, the left and right temporal regions, and the parietal lobe (p < 0.05). These findings provide a basis for further understanding neural mechanisms in the brain during the shooting preparation phase under visually and auditorily restricted conditions.

Early modulations of neural oscillations during the processing of emotional body language.

The processing of threat-related emotional body language (EBL) has been shown to engage sensorimotor cortical areas early on and induce freezing in the observers' motor system, particularly when observing fearful EBL. To provide insights into the interplay between somatosensory and motor areas during observation of EBL, here, we used high-density electroencephalography (hd-EEG) in healthy humans while they observed EBL stimuli involving fearful and neutral expressions. To capture early sensorimotor brain response, we focused on P100 fronto-central event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/ERS) in the mu-alpha (8-13 Hz) and lower beta (13-20 Hz) bands over the primary motor (M1) and somatosensory (S1) cortices. Source-level ERP and ERD/ERS analyses were conducted using eLORETA. Results revealed higher P100 amplitudes in motor and premotor channels for 'Neutral' compared with 'Fear'. Additionally, analysis of ERD/ERS showed increased beta band desynchronization in M1 for 'Neutral', and the opposite pattern in S1. Source-level estimation showed significant differences between conditions mainly observed in the beta band over sensorimotor areas. These findings provide high-temporal resolution evidence suggesting that seeing fearful EBL induces early activation of somatosensory areas, which in turn could suppress M1 activity. These findings highlight early dynamics within the observer's sensorimotor system and hint at a sensorimotor mechanism supporting freezing during the processing of EBL.

Development of Brain-Computer Interface Technology Using Scalp Electroencephalography

Studies on electroencephalography-based brain computer interface (EEG-based BCI) has been widely studied since around 2000. In the studies, three methods, event-related desynchronization/synchronization (ERD/ERS) during a motor imagery task, a P300 component of event-related potential, and steady state visual evoked potential (SSVEP) has been studied as standard methods. Since around 2013, with the acceleration of research in the United States and other countries, there has been a wide variety of studies that is not limited to the standard methods, such as research and development toward practical application and studies on extracting new brain activity signals. Some of the research results have been developed as commercial products. In this paper, after reviewing the studies on EEG-based BCI, topics in the research and development toward practical application such as new EEG systems and artifact rejection methods are introduced, topics in the studies on extracting new brain activity signals such as applying deep learning, emotion estimation and visual image reconstruction are also introduced, and topics in combination of BCI and information and communications technology and combination of BCI and XR technology are introduced. Finally, prospects of BCI technology are discussed.

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.

Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal. To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives. Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks. All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.

MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements.

Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.

A compact multi-branch 1D convolutional neural network for EEG-based motor imagery classification

Motor imagery (MI) EEG signals are considered a promising paradigm for BCI systems that enable humans to communicate with the outside world through the brain and have a wide range of applications to improve patients' quality of life with muscle or nerve damage. Due to the low signal-to-noise ratio of the acquired EEG signals, it is challenging to decode the intent accurately and even more challenging to decode the raw EEG signals. Currently, there is no deep learning method to achieve high classification performance in decoding raw EEG signals. We propose a new end-to-end network for decoding MI EEG signals, Compact Multi-Branch One-dimensional Convolutional Neural Network (CMO-CNN), without some pre-processing such as filtering, using the original EEG signals. The 1D convolution is used as the feature extractor to extract diverse and multi-level features for fusion using different filter scales and depths of different branches. 1D Squeeze-and-Excitation blocks (SE-blocks) and shortcut connections are added to further improve the generalization and robustness of the network. 83.92% and 87.19% classification accuracies were achieved in the BCI Competition IV-2a and the BCI Competition IV-2b datasets. An 8% improvement to 63.34% was achieved in the cross-subject test, demonstrating that our proposed CMO-CNN outperforms the current state-of-the-art methods. Visual analysis of the network shows that the proposed model can accurately learn the event-related desynchronization/synchronization (ERD/ERS) phenomenon in the signal, and 1D convolution is actively used for feature extraction suitable for feature extraction of the original EEG signal.

뇌-컴퓨터 인터랙션 오탐 저감을 위한 뇌파-턱 근전도 통합 신호 제어 프로토콜 개발

Objective: The purpose of this study was to investigate whether electromyogram (EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).<BR><BR>Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.<BR><BR>Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.<BR><BR>Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.<BR><BR>Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.<BR><BR>Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

Mirror visual feedback during unilateral finger movements is related to the desynchronization of cortical electroencephalographic somatomotor alpha rhythms.

Using a mirror adequately oriented, the motion of just one hand induces the illusion of the movement with the other hand. Here, we tested the hypothesis that such a mirror phenomenon may be underpinned by an electroencephalographic (EEG) event-related desynchronization/synchronization (ERD/ERS) of central alpha rhythms (around 10Hz) as a neurophysiological measure of the interactions among cerebral cortex, basal ganglia, and thalamus during movement preparation and execution. Eighteen healthy right-handed male participants performed standard auditory-triggered unilateral (right) or bilateral finger movements in the No Mirror (M-) conditions. In the Mirror (M+) condition, the unilateral right finger movements were performed in front of a mirror oriented to induce the illusion of simultaneous left finger movements. EEG activity was recorded from 64 scalp electrodes, and the artifact-free event-related EEG epochs were used to compute alpha ERD. In the M- conditions, a bilateral prominent central alpha ERD was observed during the bilateral movements, while left central alpha ERD and right alpha ERS were seen during unilateral right movements. In contrast, the M+ condition showed significant bilateral and widespread alpha ERD during the unilateral right movements. These results suggest that the above illusion of the left movements may be related to alpha ERD measures reflecting excitatory desynchronizing signals in right lateral premotor and primary somatomotor areas possibly in relation to basal ganglia-thalamic loops.

Neural correlates of texture perception during active touch

Previous studies have shown attenuation of cortical oscillations over bilateral sensorimotor cortex areas during passive perception of smooth textures applied to the skin. However, humans typically explore surfaces using dynamic hand movements. As movements may both modulate texture-related cortical activity and induce movement-related cortical activation, data from passive texture perception cannot be extrapolated to active texture perception. In the present study, we used electroencephalography to investigate cortical oscillatory changes during texture perception throughout active touch exploration. Three natural textured stimuli were selected: smooth silk, soft brushed cotton, and rough hessian. Texture samples were mounted on a purpose-built touch sensor which measured the load and position of the index finger, whilst electroencephalography from 129 channels recorded oscillatory brain activity. The data were fused to investigate oscillatory changes relating to active touch. Changes in oscillatory band power, event-related desynchronisation/synchronisation (ERD/ERS), were investigated in alpha (8–12 Hz) and beta (16–24 Hz) frequency bands. Active texture exploration revealed bilateral activation patterns over sensorimotor cortical areas. Beta-band ERD increased over contralateral sensorimotor regions for soft and smooth textures, and over ipsilateral sensorimotor areas for the smoothest texture. Analysis of covariance revealed that individual differences in perception of softness and smoothness were related to variations in cortical oscillatory activity. Differences may be due to increased high frequency vibrations for smooth and soft textures compared to rough. For the first time, active touch was quantified and fused with electroencephalography data streams, contributing to the understanding of the neural correlates of texture perception during active touch.

Interictal epileptiform discharges in focal epilepsy are preceded by increase in low-frequency oscillations

ObjectiveInterictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG). MethodsLow-frequency (1–8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7–46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation. ResultsAll patients exhibited statistically significant increase in delta (1–4 Hz) and/or theta (4–8 Hz) oscillations pre-IED compared to baseline [-2000 ms, −1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset. ConclusionsWe demonstrated consistently occurring, low-frequency oscillations prior to IED onset. SignificanceAs low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset.

A new 2-class unilateral upper limb motor imagery tasks for stroke rehabilitation training

The rehabilitation training based on motor imagery brain-computer interface (MI-BCI) is considered to be an effective method. We designed a new 2-class unilateral upper limb motor imagery tasks. Data from 15 healthy subjects and 10 stoke patients are collected in the study. The results of event-related desynchronization/synchronization (ERD/ERS) and power spectral density (PSD) analysis showed the significant different features on health subjects and stroke patients. The improved 2-Conv-FBCNET is used to classify Electroencephalogram (EEG) signals and the accuracy is (health: 61.0%, stroke: 59.4%). The new two types of tasks provide a new training method for MI-BCI rehabilitation training system.

Neural responses to shot changes by cut in cinematographic editing: An EEG (ERD/ERS) study.

In order to analyze and detect neural activations and inhibitions in film spectators to shot changes by cut in films, we developed a methodology based on comparisons of recorded EEG signals and analyzed the event-related desynchronization/synchronization (ERD/ERS). The aim of the research is isolating these neuronal responses from other visual and auditory features that covary with film editing. This system of comparing pairs of signals using permutation tests, the Spearman correlation, and slope analysis is implemented in an automated way through sliding windows, analyzing all the registered electrodes signals at all the frequency bands defined. Through this methodology, we are able to locate, identify, and quantify the variations in neuronal rhythms in specific cortical areas and frequency ranges with temporal precision. Our results detected that after a cut there is a synchronization in theta rhythms during the first 188 ms with left lateralization, and also a desynchronization between 250 ms and 750 ms in the delta frequency band. The cortical area where most of these neuronal responses are detected in both cases is the parietal area.

Mid-Air Tactile Sensations Evoked by Laser-Induced Plasma: A Neurophysiological Study.

This study demonstrates the feasibility of a mid-air means of haptic stimulation at a long distance using the plasma effect induced by laser. We hypothesize that the stress wave generated by laser-induced plasma in the air can propagate through the air to reach the nearby human skin and evoke tactile sensation. To validate this hypothesis, we investigated somatosensory responses in the human brain to laser plasma stimuli by analyzing electroencephalography (EEG) in 14 participants. Three types of stimuli were provided to the index finger: a plasma stimulus induced from the laser, a mechanical stimulus transferred through Styrofoam stick, and a sham stimulus providing only the sound of the plasma and mechanical stimuli at the same time. The event-related desynchronization/synchronization (ERD/S) of sensorimotor rhythms (SMRs) in EEG was analyzed. Every participant verbally reported that they could feel a soft tap on the finger in response to the laser stimulus, but not to the sham stimulus. The spectrogram of EEG evoked by laser stimulation was similar to that evoked by mechanical stimulation; alpha ERD and beta ERS were present over the sensorimotor area in response to laser as well as mechanical stimuli. A decoding analysis revealed that classification error increased when discriminating ERD/S patterns between laser and mechanical stimuli, compared to the case of discriminating between laser and sham, or mechanical and sham stimuli. Our neurophysiological results confirm that tactile sensation can be evoked by the plasma effect induced by laser in the air, which may provide a mid-air haptic stimulation method.

Spectral signature of attentional reorienting in the human brain

As we move in the environment, attention shifts to novel objects of interest based on either their sensory salience or behavioral value (reorienting). This study measures with magnetoencephalography (MEG) different properties (amplitude, onset-to-peak duration) of event-related desynchronization/synchronization (ERD/ERS) of oscillatory activity during a visuospatial attention task designed to separate activity related to reorienting vs. maintaining attention to the same location, controlling for target detection and response processes. The oscillatory activity was measured both in fMRI-defined regions of interest (ROIs) of the dorsal attention (DAN) and visual (VIS) networks, previously defined as task-relevant in the same subjects, or whole-brain in a pre-defined set of cortical ROIs encompassing the main brain networks.Reorienting attention (shift cues) as compared to maintaining attention (stay cues) produced a temporal sequence of ERD/ERS modulations at multiple frequencies in specific anatomical regions/networks. An early (∼330 ms), stronger, transient theta ERS occurred in task-relevant (DAN, VIS) and control networks (VAN, CON, FPN), possibly reflecting an alert/reset signal in response to the cue. A more sustained, behaviorally relevant, low-beta band ERD peaking ∼450 ms following shift cues (∼410 for stay cues) localized in frontal and parietal regions of the DAN. This modulation is consistent with a control signal re-routing information across visual hemifields. Contralateral vs. ipsilateral shift cues produced in occipital visual regions a stronger, sustained alpha ERD (peak ∼470 ms) and a longer, transient high beta/gamma ERS (peak ∼490 ms) related to preparatory visual modulations in advance of target occurrence.This is the first description of a cascade of oscillatory processes during attentional reorienting in specific anatomical regions and networks. Among these processes, a behaviorally relevant beta desynchronization in the FEF is likely associated with the control of attention shifts.

Enhanced arm swing improves Parkinsonian gait with EEG power modulations resembling healthy gait

BackgroundThe supplementary motor area (SMA) is implicated in stereotypic multi-limb movements such as walking with arm swing. Gait difficulties in Parkinson's Disease (PD) include reduced arm swing, which is associated with reduced SMA activity. ObjectiveTo test whether enhanced arm swing improves Parkinsonian gait and explore the role of the SMA in such an improvement. MethodsCortical activity and gait characteristics were assessed by ambulant EEG, accelerometers and video recordings in 27 PD patients with self-reported gait difficulties and 35 healthy participants when walking normally. Within these two groups, 19 PD patients additionally walked with enhanced arm swing and 30 healthy participants walked without arm swing. Power changes across the EEG frequency spectrum were assessed by Event Related Spectral Perturbation analysis of recordings from Fz over the putative SMA and gait analysis was performed. ResultsBaseline PD gait, characterized by reduced arm swing among other features, exhibited reduced within-step Event Related Desynchronization (ERD)/Synchronization (ERS) alternation (Fz; 20–50Hz), accompanied by a reduced step length and walking speed. All became similar to normal gait when patients walked with enhanced arm swing. When healthy controls walked without arm swing, their alternating ERD-ERS pattern decreased, mimicking baseline PD gait. ConclusionEnhanced arm swing may serve as a driving force to overcome impaired gait control in PD patients by restoring reduced ERD-ERS alternation over the putative SMA. Accompanied by increased step length and walking speed, this provides a neural underpinning of arm swing as an effective rehabilitation concept for improving Parkinsonian gait.

Can a highly accurate multi-class SSMVEP BCI induce sensory-motor rhythm in the sensorimotor area?

Objective. Different visual stimuli might have different effects on the brain, e.g. the change of brightness, non-biological movement and biological movement. Approach. In this study, flicker, checkerboard and gaiting stimuli were chosen as visual stimuli to investigate whether steady-state motion visual evoked potential (SSMVEP) effect on the sensorimotor area for rehabilitation. The gaiting stimulus was designed as the gaiting sequence of a human. The hypothesis is that only observing the designed gaiting stimulus would simultaneously induce: (1) SSMVEP in the occipital area, similarly to an SSVEP stimulus; and (2) sensorimotor rhythm (SMR) in the primary sensorimotor area, because such action observation could activate the mirror neuron system. Canonical correlation analysis was used to detect SSMVEP from occipital electroencephalograms (EEG), and event-related spectral perturbation was used to identify SMR in the EEG from the sensorimotor area. Main results. The results showed that the designed gaiting stimulus-induced SSMVEP, with classification accuracies of 88.9 ± 12.0% in a four-class scenario. More importantly, it induced clear and sustained event-related desynchronization/synchronization (ERD/ERS), while no ERD/ERS could be observed when the other two SSVEP stimuli were used. Further, for participants with a sufficiently clear SSMVEP pattern (classification accuracy >85%), the ERD index values in the mu-beta band induced by the proposed gaiting stimulus were statistically different from those of the other two types of stimulus. Significance. Therefore, a novel brain–computer interface (BCI) based on the designed stimulus has potential in neurorehabilitation applications because it simultaneously has the high accuracy of an SSMVEP (sim90% accuracy in a four-class setup) and the ability to activate the sensorimotor area.

Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user's motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications.

Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation.

Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns.

Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.

The global Beta test for hidden periodicities in signals and its extensions to multivariate systems

Background and objectiveThere are many phenomena that lead to changes in the power spectrum of a given signal, and their detection has been a challenge that has received considerable attention over the years. Objective Response Detection (ORD) techniques are a set of tools that perform automated tests for such a task, allowing thus to automatically track changes in the spectrum. The performance of these detectors is affected by the signal-to-noise ratio (SNR) of the recorded signal as well as the length of the available data. The Global F Test (GFT) is a promising detector that can be used to test whether there is a statistically significant difference between the spectrum before and during an event. In fact, this detector has proved useful in the detection of event-related desynchronization/synchronization (ERD/ERS), where only amplitude, but not the phase, changes are locked to the stimulus. In order to improve the statistical power of the GFT (for the same length of recording), multiple channels recorded simultaneously can be included. This concept is called Multivariate Response Detection. The aim of the current work is to extend the GFT to the multivariate (multichannel) case. MethodsFirstly, the single channel normalization of the GFT is presented as a new ORD detector - the global Beta test (GBT). After that, three multivariate extensions of this new test are derived. The critical values used in the detection of spectral changes are obtained by using theoretical distributions, and where this is intractable, by means of Monte Carlo simulations. The probability of detection (PD) of each technique was estimated using simulation and was used in order to compare the detectors performance. A practical example with the electroencephalogram (EEG) from 10 volunteers under intermittent photic stimulation was also provided. ResultsThe statistics under both the null and alternative hypothesis could be obtained for all detectors. Simulated results for PD demonstrate the strong potential of the proposed method and the performances in EEG data are always improved with increasing number of signals. ConclusionIf more than one signal is available, then the multivariate extensions may provide significant benefit compared to the original GFT.