Corticomuscular co-activation based hybrid brain-computer interface for motor recovery monitoring

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)

Enhanced lower-limb motor imagery by kinesthetic illusion

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p &lt; 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% &gt; 75.31%, and average F1_score for all subjects: 76.4% &gt; 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system

Electroencephalographic recording of the movement-related cortical potential in ecologically-valid movements:A scoping review

The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson’s Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Analysis of sensorimotor rhythms based on lower-limbs motor imagery for brain-computer interface

Over recent years significant advancements in the field of assistive technologies have been observed. One of the paramount needs for the development and advancement that urged researchers to contribute in the field other than congenital or diagnosed chronic disorders, is the rising number of affectees from accidents, natural calamity (due to climate change), or warfare, worldwide resulting in spinal cord injuries (SCI), neural disorder, or amputation (interception) of limbs, that impede a human to live a normal life. In addition to this, more than ten million people in the world are living with some form of handicap due to the central nervous system (CNS) disorder, which is precarious. Biomedical devices for rehabilitation are the center of research focus for many years. For people with lost motor control, or amputation, but unscathed sensory control, instigation of control signals from the source, i.e. electrophysiological signals, is vital for seamless control of assistive biomedical devices. Control signals, i.e. motion intentions, arouse&amp;nbsp;&amp;nbsp;&amp;nbsp; in the sensorimotor cortex of the brain that can be detected using invasive or non-invasive modality. With non-invasive modality, the electroencephalography (EEG) is used to record these motion intentions encoded in electrical activity of the cortex, and are deciphered to recognize user intent for locomotion. They are further transferred to the actuator, or end effector of the assistive device for control purposes. This can be executed via the brain-computer interface (BCI) technology. BCI is an emerging research field that establishes a real-time bidirectional connection between the human brain and a computer/output device. Amongst its diverse applications, neurorehabilitation to deliver sensory feedback and brain controlled biomedical devices for rehabilitation are most popular. While substantial literature on control of upper-limb assistive technologies controlled via BCI is there, less is known about the lower-limb (LL) control of biomedical devices for navigation or gait assistance via BCI. The types&amp;nbsp; of EEG signals compatible with an independent BCI are the oscillatory/sensorimotor rhythms (SMR) and event-related potential (ERP). These signals have successfully been used in BCIs for navigation control of assistive devices. However, ERP paradigm accounts for a voluminous setup for stimulus presentation to the user during operation of BCI assistive device. Contrary to this, the SMR does not require large setup for activation of cortical activity; it instead depends on the motor imagery (MI) that is produced synchronously or asynchronously by the user. MI is a covert cognitive process also termed kinaesthetic motor imagery (KMI) and elicits clearly after rigorous training trials, in form of event-related desynchronization (ERD) or synchronization (ERS), depending on imagery activity or resting period. It usually comprises of limb movement tasks, but is not limited to it in a BCI paradigm. In order to produce detectable features that correlate to the user&amp;iquest;s intent, selection of cognitive task is an important aspect to improve the performance of a BCI. MI used in BCI predominantly remains associated with the upper- limbs, particularly hands, due to the somatotopic organization of the motor cortex. The hand representation area is substantially large, in contrast to the anatomical location of the LL representation areas in the human sensorimotor cortex. The LL area is located within the interhemispheric fissure, i.e. between the mesial walls of both hemispheres of the cortex. This makes it arduous to detect EEG features prompted upon imagination of LL. Detailed investigation of the ERD/ERS in the mu and beta oscillatory rhythms during left and right LL KMI tasks is required, as the user&amp;iquest;s intent to walk is of paramount importance associated to everyday activity. This is an important area of research, followed by the improvisation of the already existing rehabilitation system that serves the LL affectees. Though challenging, solution to these issues is also imperative for the development of robust controllers that follow the asynchronous BCI paradigms to operate LL assistive devices seamlessly. This thesis focusses on the investigation of cortical lateralization of ERD/ERS in the SMR, based on foot dorsiflexion KMI and knee extension KMI separately. This research infers the possibility to deploy these features in real-time BCI by finding maximum possible classification accuracy from the machine learning (ML) models. EEG signal is non-stationary, as it is characterized by individual-to-individual and trial-to-trial variability, and a low signal-to-noise ratio (SNR), which is challenging. They are high in dimension with relatively low number of samples available for fitting ML models to the data. These factors account for ML methods that were developed into the tool of choice&amp;nbsp; to analyse single-trial EEG data. Hence, the selection of appropriate ML model for true detection of class label with no tradeoff of overfitting is crucial. The feature extraction part of the thesis constituted of testing the band-power (BP) and the common spatial pattern (CSP) methods individually. The study focused on the synchronous BCI paradigm. This was to ensure the exhibition of SMR for the possibility of a practically viable control system in a BCI. For the left vs. right foot KMI, the objective was to distinguish the bilateral tasks, in order to use them as unilateral commands in a 2-class BCI for controlling/navigating a robotic/prosthetic LL for rehabilitation. Similar was the approach for left-right knee KMI. The research was based on four main experimental studies. In addition to the four studies, the research is also inclusive of the comparison of intra-cognitive tasks within the same limb, i.e. left foot vs. left knee and right foot vs. right knee tasks, respectively (Chapter 4). This added to another novel contribution towards the findings based on comparison of different tasks within the same LL. It provides basis to increase the dimensionality of control signals within one BCI paradigm, such as a BCI-controlled LL assistive device with multiple degrees of freedom (DOF) for restoration of locomotion function. This study was based on analysis of statistically significant mu ERD feature using BP feature extraction method. The first stage of this research comprised of the left vs. right foot KMI tasks, wherein the ERD/ERS that elicited in the mu-beta rhythms were analysed using BP feature extraction method (Chapter 5). Three individual features, i.e. mu ERD, beta ERD, and beta ERS were investigated on EEG topography and time-frequency (TF) maps, and average time course of power percentage, using the common average reference and bipolar reference methods. A comparative study was drawn for both references to infer the optimal method. This was followed by ML, i.e. classification of the three feature vectors (mu ERD, beta ERD, and beta ERS), using linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbour (KNN) algorithms, separately. Finally, the multiple correction statistical tests were done, in order to predict maximum possible classification accuracy amongst all paradigms for the most significant feature. All classifier models were supported with the statistical techniques of k-fold cross validation and evaluation of area under receiver-operator characteristic curves (AUC-ROC) for prediction of the true class label. The highest classification accuracy of 83.4% &amp;plusmn; 6.72 was obtained with KNN model for beta ERS feature. The next study was based on enhancing the classification accuracy obtained from previous study. It was based on using similar cognitive tasks as study in Chapter 5, however deploying different methodology for feature extraction and classification procedure. In the second study, ERD/ERS from mu and beta rhythms were extracted using CSP and filter bank common spatial pattern (FBCSP) algorithms, to optimize the individual spatial patterns (Chapter 6). This was followed by ML process, for which the supervised logistic regression (Logreg) and LDA were deployed separately. Maximum classification accuracy resulted in 77.5% &amp;plusmn; 4.23 with FBCSP feature vector and LDA model, with a maximum kappa coefficient of 0.55 that is in the moderate range of agreement between the two classes. The left vs. right foot discrimination results were nearly same, however the BP feature vector performed better than CSP. The third stage was based on the deployment of novel cognitive task of left vs. right knee extension KMI. Analysis of the ERD/ERS in the mu-beta rhythms was done for verification of cortical lateralization via BP feature vector (Chapter 7). Similar to Chapter 5, in this study the analysis of ERD/ERS features was done on the EEG topography and TF maps, followed by the determination of average time course and peak latency of feature occurrence. However, for this study, only mu ERD and beta ERS features were taken into consideration and the EEG recording method only comprised of common average reference. This was due to the established results from the foot study earlier, in Chapter 5, where beta ERD features showed less average amplitude. The LDA and KNN classification algorithms were employed. Unexpectedly, the left vs. right knee KMI reflected the highest accuracy of 81.04% &amp;plusmn; 7.5 and an AUC-ROC = 0.84, strong enough to be used in a real-time BCI as two independent control features. This was using KNN model for beta ERS feature. The final study of this research followed the same paradigm as used in Chapter 6, but for left vs. right knee KMI cognitive task (Chapter 8). Primarily this study aimed at enhancing the resulting accuracy from Chapter 7, using CSP and FBCSP methods with Logreg and LDA models respectively. Results were in accordance with those of the already established foot KMI study, i.e. BP feature vector performed better than the CSP. Highest classification accuracy of 70.00% &amp;plusmn; 2.85 with kappa score of 0.40 was obtained with Logreg using FBCSP feature vector. Results stipulated the utilization of ERD/ERS in mu and beta bands, as independent control features for discrimination of bilateral foot or the novel bilateral knee KMI tasks. Resulting classification accuracies implicate that any 2-class BCI, employing unilateral foot, or knee KMI, is suitable for real-time implementation. In conclusion, this thesis demonstrates the possible EEG pre-processing, feature extraction and classification methods to instigate a real-time BCI from the conducted studies. Following this, the critical aspects of latency in information transfer rate, SNR, and tradeoff between dimensionality and overfitting needs to be taken care of, during design of real-time BCI controller. It also highlights that there is a need for consensus over the development of standardized methods of cognitive tasks for MI based BCI. Finally, the application of wireless EEG for portable assistance is essential as it will contribute to lay the foundations of the development of independent asynchronous BCI based on SMR

Rehabilitation of gait after stroke: a review towards a top-down approach

This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity