1. A Cortico-Muscular-Coupling based Single-Trial Detection in EEG-EMG based BCI for Personalized Neuro-Rehabilitation of Stroke Patients 1. Introduction EMBC 2015. Every year massive number of people get affected by stroke with an annual estimate of over 15 million and up to 40% of stroke survivors may suffer from permanent upper limb paralysis, which may significantly impact their quality of life and employability. Often the upper limb paralysis becomes chronic due to lack of active and engaging rehabilitation exercises over a prolonged period. This research introduces a novel approach of operating a hand-exoskeleton device, intended for neuro-rehabilitation of stroke patients, using cortico-muscular-coupling (CMC). As part of a brain-computer interface (BCI), the approach combines the electroencephalogram (EEG) signals and the electromyogram (EMG) signals to determine the coherence between them in terms of a correlation index. The CMC correlation index is used to decide the movement intension of a user (e.g. whether a user is making an effort to move the fingers), and accordingly the hand exoskeleton is triggered to provide the desired hand movement as well as a neuro-feedback to the user as part of an active and engaging physical (or physiotherapy) practice. We have also analysed the EEG signals alone for the determination of the movement intentions, as in case of patients going through acute phase after stroke the EMG activity is significantly less and consequently very low coupling between the EEG and EMG signals may be present for the determination of their effort. Moreover, to monitor the non-stationary changes often present in the EEG signals, we have used a covariate shift-detection (CSD) test to detect the occurrence of covariate shifts over time. Once, the covariate shift is detected, the BCI classifier is adapted appropriately. We have tested our proposed approaches on 13 healthy subjects. The results show an improvement in adaptive BCI over non-adaptive BCI. Whereas, CMC based method shows superiority over other EEG based methods. It also shows that the CSD based adaptive BCI is more promising than non-adaptive BCI. Anirban Chwodhury 1, Haider Raza 2, Ashish Dutta 1, Girijesh Prasad 2 anir@iitk.ac.in, raza-h@email.ulster.ac.uk, adutta@iitk.ac.in, g.prasad@ulster.ac.uk 1 IIT Kanpur, Kanpur, India; 2 ISRC, Ulster University, Magee Campus, Derry~Londonderry, N. Ireland, UK 5. Conclusion This new CMC based approach is helpful in increasing the detection accuracy, and additionally it provides an association index between the brain and muscle signals. This association index could also act as a measure for patients’ health condition monitoring during the course of his/her rehabilitation process. Acknowledgment This research work is supported by the UKIERI DST Thematic Partnership project "A BCI operated hand exoskeleton based neuro- rehabilitation system" (UKIERI-DST-2013-14/126). 3. Method 2. System Architecture 4. Results Fig.4: Basic architecture of the proposed rehabilitation system Fig.1: Timing Diagram of a single trial. We have investigated three different BCI classification methods. 1) EEG-based Non-Adaptive Classifier (EEG-NAC) 2) EEG-based Covariate Shift Adaptive Classifier (EEG-CSAC) 3) EEG & EMG based Cortico-Muscular-Correlation Classifier (CMC). ElectrodesPlacement 6 EEG FC3, CP3, FCz, CPz, FC4, CP4 4 EMG Flexor-Digitorum-Superficialis muscle & Flexor-Policus-Longus muscle Fig.3: Flow Chart for single-trial Fig. 7: Covariate shift in the subject S08, between training and testing input distribution for different frequency bands. (a) Mu band [8-12] Hz, and (b) Beta band [16-24]Hz T ABLE 1. RESULTS OF SHIFT - DETECTION & CLASSIFICATION ACCURACY ACROSS DIFFERENT METHODS T ABLE 2. AVERAGE CMC INDEXES THROUGH ALL TRIALS DURING TRAINING AND TESTING PHASE OF EACH SUBJECT Fig 8: Box plot of Comparison between the performances of each of the methods used for data classification. EEG- NAC, EEG-CAS and CMC is CMC based Classifier. We have tested our methods on 13 healthy subjects. The results show an average movement intension detection accuracy of 79% for the non-adaptive system, and 80% for the adaptive system. Whereas, 95% average detection accuracy is achieved in the case of CMC based approach. The CMC based method shows superiority over other EEG based methods. It also shows that the CSD based adaptive BCI is more promising than non-adaptive BCI. Fig.2: Design of Hand-Exoskeleton Fig.5: Block-diagram for the architecture of the proposed EEG-EMG based rehabilitation system Fig.6: Block diagram for CMC based approach CSP Features