Effects of neurofeedback training with an electroencephalogram-based Brain–Computer Interface for hand paralysis in patients with chronic stroke: A preliminary case series study

Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke

Recent studies have shown that scalp electroencephalogram (EEG) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients with severe hemiplegia. However, key elements in BCI architecture for functional recovery has yet to be clear. We in this study focused on the type of feedback to the patients, which is given contingently to their motor-related EEG in a BCI context. The efficacy of visual and somatosensory feedbacks was compared by a two-group study with the chronic stroke patients who are suffering with severe motor hemiplegia. Twelve patients were asked an attempt of finger opening in the affected side repeatedly, and the event-related desynchronization (ERD) in EEG of alpha and beta rhythms was monitored over bilateral parietal regions. Six patients were received a simple visual feedback in which the hand open/grasp picture on screen was animated at eye level, following significant ERD. Six patients were received a somatosensory feedback in which the motor-driven orthosis was triggered to extend the paralyzed fingers from 90 to 50°. All the participants received 1-h BCI treatment with 12–20 training days. After the training period, while no changes in clinical scores and electromyographic (EMG) activity were observed in visual feedback group after training, voluntary EMG activity was newly observed in the affected finger extensors in four cases and the clinical score of upper limb function in the affected side was also improved in three participants in somatosensory feedback group. Although the present study was conducted with a limited number of patients, these results imply that BCI training with somatosensory feedback could be more effective for rehabilitation than with visual feedback. This pilot trial positively encouraged further clinical BCI research using a controlled design.

Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability

Therapies involving new technologies such as brain-computer interfaces (BCI) are being studied to determine their potential for interventional rehabilitation after acute events such as stroke produce lasting impairments. While studies have examined the use of BCI devices by individuals with disabilities, many such devices are intended to address a specific limitation and have been studied when this limitation or disability is present in isolation. Little is known about the therapeutic potential of these devices for individuals with multiple disabilities with an acquired impairment overlaid on a secondary long-standing disability. We describe a case in which a male patient with congenital deafness suffered a right pontine ischemic stroke, resulting in persistent weakness of his left hand and arm. This patient volunteer completed four baseline assessments beginning at 4 months after stroke onset and subsequently underwent 6 weeks of interventional rehabilitation therapy using a closed-loop neurofeedback BCI device with visual, functional electrical stimulation, and tongue stimulation feedback modalities. Additional assessments were conducted at the midpoint of therapy, upon completion of therapy, and 1 month after completing all BCI therapy. Anatomical and functional MRI scans were obtained at each assessment, along with behavioral measures including the Stroke Impact Scale (SIS) and the Action Research Arm Test (ARAT). Clinically significant improvements in behavioral measures were noted over the course of BCI therapy, with more than 10 point gains in both the ARAT scores and scores for the SIS hand function domain. Neuroimaging during finger tapping of the impaired hand also showed changes in brain activation patterns associated with BCI therapy. This case study demonstrates the potential for individuals who have preexisting disability or possible atypical brain organization to learn to use a BCI system that may confer some rehabilitative benefit.

Modulation of event-related desynchronization during kinematic and kinetic hand movements

Background

Event-related desynchronization/synchronization (ERD/ERS) is a relative power decrease/increase of electroencephalogram (EEG) in a specific frequency band during physical motor execution and mental motor imagery, thus it is widely used for the brain-computer interface (BCI) purpose. However what the ERD really reflects and its frequency band specific role have not been agreed and are under investigation. Understanding the underlying mechanism which causes a significant ERD would be crucial to improve the reliability of the ERD-based BCI. We systematically investigated the relationship between conditions of actual repetitive hand movements and resulting ERD.

Methods

Eleven healthy young participants were asked to close/open their right hand repetitively at three different speeds (Hold, 1/3 Hz, and 1 Hz) and four distinct motor loads (0, 2, 10, and 15 kgf). In each condition, participants repeated 20 experimental trials, each of which consisted of rest (8–10 s), preparation (1 s) and task (6 s) periods. Under the Hold condition, participants were instructed to keep clenching their hand (i.e., isometric contraction) during the task period. Throughout the experiment, EEG signals were recorded from left and right motor areas for offline data analysis. We obtained time courses of EEG power spectrum to discuss the modulation of mu and beta-ERD/ERS due to the task conditions.

Results

We confirmed salient mu-ERD (8–13 Hz) and slightly weak beta-ERD (14–30 Hz) on both hemispheres during repetitive hand grasping movements. According to a 3 × 4 ANOVA (speed × motor load), both mu and beta-ERD during the task period were significantly weakened under the Hold condition, whereas no significant difference in the kinetics levels and interaction effect was observed.

Conclusions

This study investigates the effect of changes in kinematics and kinetics on resulting ERD during repetitive hand grasping movements. The experimental results suggest that the strength of ERD may reflect the time differentiation of hand postures in motor planning process or the variation of proprioception resulting from hand movements, rather than the motor command generated in the down stream, which recruits a group of motor neurons.

Effect of neurofeedback and electromyographic-biofeedback therapy on improving hand function in stroke patients

PURPOSE

The aim of the present study was to evaluate the effect of applying electroencephalogram (EEG) biofeedback (neurobiofeedback) or electromyographic (EMG) biofeedback to conventional occupational therapy (OT) on improving hand function in stroke patients.

METHODS

This study was designed as a preliminary clinical trial. Thirty patients with stroke were entered the study. Hand function was evaluated by Jebsen Hand Function Test pre and post intervention. Patients were allocated to 3 intervention cohorts: (1) OT, (2) OT plus EMG-biofeedback therapy, and (3) OT plus neurofeedback therapy. All patients received 10 sessions of conventional OT. Patients in cohorts 2 and 3 also received EMG-biofeedback and neurofeedback therapy, respectively. EMG-biofeedback therapy was performed to strengthen the abductor pollicis brevis (APB) muscle. Neurofeedback training was aimed at enhancing sensorimotor rhythm after mental motor imagery.

RESULTS

Hand function was improved significantly in the 3 groups. The spectral power density of the sensorimotor rhythm band in the neurofeedback group increased after mental motor imagery. Maximum and mean contraction values of electrical activities of the APB muscle during voluntary contraction increased significantly after EMG-biofeedback training.

CONCLUSION

Patients in the neurofeedback and EMG-biofeedback groups showed hand improvement similar to conventional OT. Further studies are suggested to assign the best protocol for neurofeedback and EMG-biofeedback therapy.

Classification of motor imagery performance in acute stroke

OBJECTIVE

Effective motor imagery performance, seen as strong suppression of the sensorimotor rhythm, is the key element in motor imagery therapy. Therefore, optimization of methods to classify whether the subject is performing the imagery task is a prerequisite. An optimal classification method should have high performance accuracy and use a small number of channels. We investigated the additional benefit of the common spatial pattern filtering (CSP) to a linear discriminant analysis (LDA) classifier, for different channel configurations.

METHODS

Ten hemispheric acute stroke patients and 11 healthy subjects were included. EEGs were recorded using 60 channels. The classifier was trained with a motor execution task. For both healthy controls and patients, analysis of recordings was initially limited to 3 and 11 electrodes recording from the motor cortex area, and later repeated using 45 electrodes.

RESULTS

No significant improvement on the addition of CSP to LDA was found (in both cases, the area under the receiving operating characteristic (AU-ROC) ≈ 0.70 (acceptable)). We then repeated the LDA+CSP method on recordings of 45 electrodes, since the use of imagery neuronal circuits may well extend beyond the motor area. AU-ROC rose to 0.90, but no virtual 'most responsible' electrode was observed. Finally, in mild-to-moderate stroke patients we could successfully use the EEG data recorded from the healthy hemisphere to train the classifier (AU-ROC ≈ 0.70).

SIGNIFICANCE

Including only the channels on the unaffected motor cortex is sufficient to train a classifier.

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.

Neurophysiological substrates of stroke patients with motor imagery-based Brain-Computer Interface training

We investigated the efficacy of motor imagery-based Brain Computer Interface (MI-based BCI) training for eight stroke patients with severe upper extremity paralysis using longitudinal clinical assessments. The results were compared with those of a control group (n = 7) that only received FES (Functional Electrical Stimulation) treatment besides conventional therapies. During rehabilitation training, changes in the motor function of the upper extremity and in the neurophysiologic electroencephalographic (EEG) were observed for two groups. After 8 weeks of training, a significant improvement in the motor function of the upper extremity for the BCI group was confirmed (p < 0.05 for ARAT), simultaneously with the activation of bilateral cerebral hemispheres. Additionally, event-related desynchronization (ERD) of the affected sensorimotor cortexes (SMCs) was significantly enhanced when compared to the pretraining course, which was only observed in the BCI group (p < 0.05). Furthermore, the activation of affected SMC and parietal lobe were determined to contribute to motor function recovery (p < 0.05). In brief, our findings demonstrate that MI-based BCI training can enhance the motor function of the upper extremity for stroke patients by inducing the optimal cerebral motor functional reorganization.

Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation

Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.

Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex

There is increasing interest in electroencephalogram (EEG)-based brain-computer interface (BCI) as a tool for rehabilitation of upper limb motor functions in hemiplegic stroke patients. This type of BCI often exploits mu and beta oscillations in EEG recorded over the sensorimotor areas, and their event-related desynchronization (ERD) following motor imagery is believed to represent increased sensorimotor cortex excitability. However, it remains unclear whether the sensorimotor cortex excitability is actually correlated with ERD. Thus we assessed the association of ERD with primary motor cortex (M1) excitability during motor imagery of right wrist movement. M1 excitability was tested by motor evoked potentials (MEPs), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF) with transcranial magnetic stimulation (TMS). Twenty healthy participants were recruited. The participants performed 7 s of rest followed by 5 s of motor imagery and received online visual feedback of the ERD magnitude of the contralateral hand M1 while performing the motor imagery task. TMS was applied to the right hand M1 when ERD exceeded predetermined thresholds during motor imagery. MEP amplitudes, SICI, and ICF were recorded from the agonist muscle of the imagined hand movement. Results showed that the large ERD during wrist motor imagery was associated with significantly increased MEP amplitudes and reduced SICI but no significant changes in ICF. Thus ERD magnitude during wrist motor imagery represents M1 excitability. This study provides electrophysiological evidence that a motor imagery task involving ERD may induce changes in corticospinal excitability similar to changes accompanying actual movements.

Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

EEG-based classification of imaginary left and right foot movements using beta rebound

OBJECTIVE

The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm.

METHODS

We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated.

RESULTS

Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis).

CONCLUSION

These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery.

SIGNIFICANCE

By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot.

Daily training with realistic visual feedback improves reproducibility of event-related desynchronisation following hand motor imagery

OBJECTIVE

Few brain-computer interface (BCI) studies have addressed learning mechanisms by exposure to visual feedback that elicits scalp electroencephalogram. We examined the effect of realistic visual feedback of hand movement associated with sensorimotor rhythm.

METHODS

Thirty-two healthy participants performed in five daily training in which they were shown motor imagery of their dominant hand. Participants were randomly assigned to 1 of 4 experimental groups receiving different types of visual feedback on event-related desynchronisation (ERD) derived over the contralateral sensorimotor cortex: no feedback as a control, bar feedback with changing bar length, anatomically incongruent feedback in which the hand open/grasp picture on screen was animated at eye level, and anatomically congruent feedback in which the same hand open/grasp picture was animated on the screen overlaying the participant's hand.

RESULTS

Daily training with all types of visual feedback induced more robust ERD than the no feedback condition (p < 0.05). The anatomically congruent feedback produced the highest reproducibility of ERD with the smallest inter-trial variance (p < 0.05).

CONCLUSION

Realistic feedback training is a suitable method to acquire the skill to control a BCI system.

SIGNIFICANCE

This finding highlights the possibility of improvement of reproducibility of ERD and can help to use BCI techniques.

Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters

OBJECTIVE

Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys.

APPROACH

Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis, and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time.

MAIN RESULTS

Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The 'position-to-velocity' transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys' online performance significantly improved across days of closed-loop training.

SIGNIFICANCE

Most published BMI studies that use electrocorticographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a substantial improvement in decoding accuracy could be attained by using our new version of the CSP algorithm that extends the traditional CSP method for use with continuous limb movement data.

Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses

OBJECTIVE

Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings.

APPROACH

A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed.

MAIN RESULTS

Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively.

SIGNIFICANCE

The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

[Brain-machine interface (BMI) - application to neurological disorders]

Brain-machine interface (BMI) is a new technology to receive input from the brain which is translated to operate a computer or other external device in real time. After significant progress during the recent 10 years, this technology is now very close to the clinical use to restore neural functions of patients with severe neurologic impairment. This technology is also a strong tool to investigate the mode of neuro-signal processing in the brain and to understand the mechanism of neural dysfunction which leads to the development of novel neurotechnology for the treatment of various sorts of neurological disorders.

Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study

BACKGROUND

We developed an electroencephalogram-based brain computer interface system to modulate functional electrical stimulation (FES) to the affected tibialis anterior muscle in a stroke patient. The intensity of FES current increased in a stepwise manner when the event-related desynchronization (ERD) reflecting motor intent was continuously detected from the primary cortical motor area.

METHODS

We tested the feasibility of the ERD-modulated FES system in comparison with FES without ERD modulation. The stroke patient who presented with severe hemiparesis attempted to perform dorsiflexion of the paralyzed ankle during which FES was applied either with or without ERD modulation.

RESULTS

After 20 minutes of training, the range of movement at the ankle joint and the electromyography amplitude of the affected tibialis anterior muscle were significantly increased following the ERD-modulated FES compared with the FES alone.

CONCLUSIONS

The proposed rehabilitation technique using ERD-modulated FES for stroke patients was feasible. The system holds potentials to improve the limb function and to benefit stroke patients.

Analysis of extrinsic and intrinsic factors affecting event related desynchronization production

Recently there has been an increase in the number of stroke patients with motor paralysis. Appropriate re-afferent sensory feedback synchronized with a voluntary motor intention would be effective for promoting neural plasticity in the stroke rehabilitation. Therefore, BCI technology is considered to be a promising approach in the neuro-rehabilitation. To estimate human motor intention, an event-related desynchronization (ERD), a feature of electroencephalogram (EEG) evoked by motor execution or motor imagery is usually used. However, there exists various factors that affect ERD production, and its neural mechanism is still an open question. As a preliminary stage, we evaluate mutual effects of intrinsic (voluntary motor imagery) and extrinsic (visual and somatosensory stimuli) factors on the ERD production. Experimental results indicate that these three factors are not always additively interacting with each other and affecting the ERD production.