Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study.

BACKGROUND AND AIMS: Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs. METHODS: We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE). RESULTS: The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R = - 0.423, P = 0.009) but not with grasp MA (R = - 0.058, P = 0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n = 19) and group 2 (Brunnstrom recovery stages between III to VI, n = 23). No difference during the grasping task (t = 0.091, P = 0.928), but a significant difference during the open task (t = 2.156, P = 0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere. CONCLUSIONS: The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.

A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control.

While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.

Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study.

Background: Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke. Materials and methods: Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing. Results: The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session. Conclusion: Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery. Trial registration number: ChiCTR2100044492.

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study.

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists' participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients' motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

Relation Between Sensorimotor Rhythm During Motor Attempt/Imagery and Upper-Limb Motor Impairment in Stroke.

Motor attempt (MA)/motor imagery (MI)-based brain-computer interface (BCI) is a newly developing rehabilitation technology for motor impairment. This study aims to explore the relationship between electroencephalography sensorimotor rhythm and motor impairment to provide reference for a BCI design. Twenty-eight stroke survivors with varying levels of motor dysfunction and spasticity status in the subacute or chronic stage were enrolled in the study to perform MA and MI tasks. Event-related desynchronization (ERD)/event-related synchronization (ERS) during and immediately after motor tasks were calculated. The Fugl-Meyer assessment scale (FMA) and the modified Ashworth scale (MAS) were applied to characterize upper-limb motor dysfunction and spasticity. There was a positive correlation between FMA total scores and ERS in the contralesional hemisphere in the MI task (P < .05) and negative correlations between FMA total scores and ERD in both hemispheres in the MA task (P < .05). Negative correlations were found between MAS scores of wrist flexors and ERD in the ipsilesional hemisphere (P < .05) in the MA task. It suggests that motor dysfunction may be more correlated to ERS in the MI task and to ERD in the MA task while spasticity may be more correlated to ERD in the MA task.

The Differences Between Motor Attempt and Motor Imagery in Brain-Computer Interface Accuracy and Event-Related Desynchronization of Patients With Hemiplegia.

Background: Motor attempt and motor imagery (MI) are two common motor tasks used in brain-computer interface (BCI). They are widely researched for motor rehabilitation in patients with hemiplegia. The differences between the motor attempt (MA) and MI tasks of patients with hemiplegia can be used to promote BCI application. This study aimed to explore the accuracy of BCI and event-related desynchronization (ERD) between the two tasks. Materials and Methods: We recruited 13 patients with stroke and 3 patients with traumatic brain injury, to perform MA and MI tasks in a self-control design. The BCI accuracies from the bilateral, ipsilesional, and contralesional hemispheres were analyzed and compared between different tasks. The cortical activation patterns were evaluated with ERD and laterality index (LI). Results: The study showed that the BCI accuracies of MA were significantly (p < 0.05) higher than MI in the bilateral, ipsilesional, and contralesional hemispheres in the alpha-beta (8-30 Hz) frequency bands. There was no significant difference in ERD and LI between the MA and MI tasks in the 8-30 Hz frequency bands. However, in the MA task, there was a negative correlation between the ERD values in the channel CP1 and ipsilesional hemispheric BCI accuracies (r = -0.552, p = 0.041, n = 14) and a negative correlation between the ERD values in channel CP2 and bilateral hemispheric BCI accuracies (r = -0.543, p = 0.045, n = 14). While in the MI task, there were negative correlations between the ERD values in channel C4 and bilateral hemispheric BCI accuracies (r = -0.582, p = 0.029, n = 14) as well as the contralesional hemispheric BCI accuracies (r = -0.657, p = 0.011, n = 14). As for motor dysfunction, there was a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 8-13 Hz (r = 0.565, p = 0.035, n = 14) in the MA task and a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 13-30 Hz (r = 0.558, p = 0.038, n = 14) in the MI task. Conclusion: The MA task may achieve better BCI accuracy but have similar cortical activations with the MI task. Cortical activation (ERD) may influence the BCI accuracy, which should be carefully considered in the BCI motor rehabilitation of patients with hemiplegia.

A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy.

Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands. Based on the inference confidence and user's characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm.Main results.Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately.Significance.In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.

State of the Art of Non-Invasive Electrode Materials for Brain-Computer Interface.

The brain-computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.

Electrical stimulation-induced SSSEP as an objective index to evaluate the difference of tactile acuity between the left and right hand.

OBJECTIVE: The objective of this study is to propose an objective index to evaluate the difference of tactile acuity between the left and right hand based on steady-state somatosensory evoked potential (SSSEP). APPROACH: Two kinds of tactile sensations (vibration and pressure) with three levels of intensities (low/medium/high) were evoked on two finger areas of the left or right hand (thumb and index for healthy hands, thumb and index-projected areas for disabled hands) via transcutaneous electrical nerve stimulation (TENS). Three forearm amputees and 13 able-bodied subjects were recruited to discriminate the specific level and area of the applied stimulation. Electroencephalography was adopted to simultaneously record the somatosensory cortex response to TENS. We assessed the discrimination performance (discrimination accuracy rate (AR) and response time (RT)) to quantify the tactile acuity, while the evoked SSSEP was synchronously analyzed. Linear regression analyses were performed between the difference of SSSEP amplitudes and the difference of discrimination performance for the left and right hand stimulation. MAIN RESULTS: Frequency domain analysis revealed that SSSEP amplitude increased with the increase of the stimulation intensity. There were positive correlations between the difference of SSSEP amplitudes and the difference of ARs for the left and right hand stimulation in the sensations of vibration (R 2 = 0.6389 for able-bodied subjects, R 2 = 0.5328 for amputees) and pressure (R 2 = 0.6102 for able-bodied subjects, R 2 = 0.5452 for amputees), respectively. Significance The SSSEP amplitude could be used as an objective index to evaluate the difference of the tactile acuity between the left and right hand and has the potential to be applied in sensory rehabilitation for amputees or stroke patients.

Electroencephalography Mu Rhythm Changes and Decreased Spasticity After Repetitive Peripheral Magnetic Stimulation in Patients Following Stroke.

Background: Spasticity is common among patients with stroke. Repetitive peripheral magnetic stimulation (rPMS) is a painless and noninvasive therapy that is a promising approach to reducing spasticity. However, the central mechanism of this therapy remains unclear. Changes in cortical activity and decreased spasticity after rPMS intervention require further exploration. The aim of this study was to explore the electroencephalography (EEG) mu rhythm change and decrease in spasticity after rPMS intervention in patients with stroke. Materials and methods: A total of 32 patients with spasticity following stroke were recruited in this study and assigned to the rPMS group (n = 16) or sham group (n = 16). The modified Ashworth scale, modified Tardieu scale, and Fugl-Meyer assessment of the upper extremity were used to assess changes in upper limb spasticity and motor function. Before and after the rPMS intervention, EEG evaluation was performed to detect EEG mu rhythm changes in the brain. Results: After one session of rPMS intervention, spasticity was reduced in elbow flexors (p < 0.05) and wrist flexors (p < 0.05). Upper limb motor function measured according to the Fugl-Meyer assessment was improved (p < 0.05). In the rPMS group, the power of event-related desynchronization decreased in the mu rhythm band (8-12 Hz) in the contralesional hemisphere (p < 0.05). Conclusions: The results indicate that rPMS intervention reduced spasticity. Cortical activity changes may suggest this favorable change in terms of its neurological effects on the central nervous system.

Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain-Computer Interface With Exoskeleton Feedback.

BACKGROUND: Brain-computer interface (BCI) has been regarded as a newly developing intervention in promoting motor recovery in stroke survivors. Several studies have been performed in chronic stroke to explore its clinical and subclinical efficacy. However, evidence in subacute stroke was poor, and the longitudinal sensorimotor rhythm changes in subacute stroke after BCI with exoskeleton feedback were still unclear. MATERIALS AND METHODS: Fourteen stroke patients in subacute stage were recruited and randomly allocated to BCI group (n = 7) and the control group (n = 7). Brain-computer interface training with exoskeleton feedback was applied in the BCI group three times a week for 4 weeks. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) scale was used to assess motor function improvement. Brain-computer interface performance was calculated across the 12-time interventions. Sensorimotor rhythm changes were explored by event-related desynchronization (ERD) changes and topographies. RESULTS: After 1 month BCI intervention, both the BCI group (p = 0.032) and the control group (p = 0.048) improved in FMA-UE scores. The BCI group (12.77%) showed larger percentage of improvement than the control group (7.14%), and more patients obtained good motor recovery in the BCI group (57.1%) than did the control group (28.6%). Patients with good recovery showed relatively higher online BCI performance, which were greater than 70%. And they showed a continuous improvement in offline BCI performance and obtained a highest value in the last six sessions of interventions during BCI training. However, patients with poor recovery reached a platform in the first six sessions of interventions and did not improve any more or even showed a decrease. In sensorimotor rhythm, patients with good recovery showed an enhanced ERD along with time change. Topographies showed that the ipsilesional hemisphere presented stronger activations after BCI intervention. CONCLUSION: Brain-computer interface training with exoskeleton feedback was feasible in subacute stroke patients. Brain-computer interface performance can be an index to evaluate the efficacy of BCI intervention. Patients who presented increasingly stronger or continuously strong activations (ERD) may obtain better motor recovery.

Neural Modulation By Repetitive Transcranial Magnetic Stimulation (rTMS) for BCI Enhancement in Stroke Patients.

Brain-computer interface (BCI) is a novel method for stroke rehabilitation. However, lacking of sufficient motor-related cortical activity greatly decreases the BCI performance in stroke patients. Interestingly, high-frequency repetitive transcranial magnetic stimulation (rTMS) has been shown to increase the cortical excitability of lesioned hemisphere in stroke patients. This stimulation effect may have benefits on the enhancement of BCI decoding. This study recruited 16 stroke patients to evaluate the stimulation effect on BCI accuracy, with 8 patients were assigned to the TMS-group and the other 8 patients were assigned to the Control-group. Patients in the TMS-group underwent 12 sessions of 10-Hz TMS interventions in four consecutive weeks, whereas no stimulation was applied during this period in the Control-group. Meanwhile, three BCI evaluation sessions were carried out in one day before, one day after, and three days after the TMS intervention, separately. The results showed that the TMS intervention significantly improved the BCI accuracy from 63.5% to 74.3% in motor imagery (MI) tasks, and from 81.9% to 91.1% in motor execution (ME) tasks. This finding provides a novel method for the cure of BCI-inefficiency problem, and may facilitate the clinical application of BCI-based stroke rehabilitation.

Tactile Stimulation Improves Sensorimotor Rhythm-based BCI Performance in Stroke Patients.

OBJECTIVE: BCI decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity in stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR)-based BCI performance by integrating motor tasks with tactile stimulation. METHODS: Eleven stroke patients were recruited for three experimental conditions, i.e., motor attempt (MA) condition, tactile stimulation (TS) condition, and tactile stimulation-assisted motor attempt (TS-MA) condition. Tactile stimulation was delivered to the paretic hand wrist during both task and idle states using a DC vibrator. RESULTS: We observed that the TS-MA condition achieved greater motor-related cortical activation (MRCA) in alpha-beta band when compared with both TS and MA conditions. Consequently, online BCI decoding accuracies between task and idle states were significantly improved from 74.5% in the MA condition to 85.1% in the TS-MA condition (p < 0.001), whereas the accuracy in the TS condition was 54.6% (approaching to the chance level of 50%). CONCLUSION: This finding demonstrates that sensory afferent from peripheral nerves benefits the neural process of sensorimotor cortex in stroke patients. With appropriate sensory stimulation, MRCA is enhanced and corresponding brain patterns are more discriminative. SIGNIFICANCE: This novel SMR-BCI paradigm shows great promise to facilitate the practical application of BCI-based stroke rehabilitation.

Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

Enhanced Motor Imagery-Based BCI Performance via Tactile Stimulation on Unilateral Hand.

Brain-computer interface (BCI) has attracted great interests for its effectiveness in assisting disabled people. However, due to the poor BCI performance, this technique is still far from daily-life applications. One of critical issues confronting BCI research is how to enhance BCI performance. This study aimed at improving the motor imagery (MI) based BCI accuracy by integrating MI tasks with unilateral tactile stimulation (Uni-TS). The effects were tested on both healthy subjects and stroke patients in a controlled study. Twenty-two healthy subjects and four stroke patients were recruited and randomly divided into a control-group and an enhanced-group. In the control-group, subjects performed two blocks of conventional MI tasks (left hand vs. right hand), with 80 trials in each block. In the enhanced-group, subjects also performed two blocks of MI tasks, but constant tactile stimulation was applied on the non-dominant/paretic hand during MI tasks in the second block. We found the Uni-TS significantly enhanced the contralateral cortical activations during MI of the stimulated hand, whereas it had no influence on activation patterns during MI of the non-stimulated hand. The two-class BCI decoding accuracy was significantly increased from 72.5% (MI without Uni-TS) to 84.7% (MI with Uni-TS) in the enhanced-group (p < 0.001, paired t-test). Moreover, stroke patients in the enhanced-group achieved an accuracy >80% during MI with Uni-TS. This novel approach complements the conventional methods for BCI enhancement without increasing source information or complexity of signal processing. This enhancement via Uni-TS may facilitate clinical applications of MI-BCI.