P186. Correlates to influence user performance in a hand motor rehabilitation task

Abstract

Introduction The power of oscillatory EEG signal components has been related to attention or mental workload. Thus it may allow to decode and track mental states related to the performance of a user on a specific task. In motor rehabilitation after stroke, knowledge about suitable or impeding brain states may be informative enough to causally influence the training performance. A prerequisite for such real-time manipulation is the ability to decode ongoing EEG signals. Solutions are provided by the field of Brain–Computer Interface (BCI) systems. We propose the application of BCI methods in the context of decoding mental states relevant for successful stroke motor rehabilitation training. Extracting oscillatory sources from a pre-trial EEG interval of the alpha band with optimized data-driven filters, we show that the sources are informative about trial-wise reaction time (RT). Methods A paradigm to assess and train hand motor function in acute and chronic stroke patients is the Sequential Visual Isometric Pinch Task (SVIPT, Reis et al., 2009). Current studies have shown that motor improvements achieved using SVIPT training will generalize to a range of hand-motor tasks. We report on simulated online results of a novel EEG-tracked SVIPT system. Specifically, we explored the usefulness of alpha band (8–14 Hz) pre-trial information (−800 to 0 ms) to predict fluctuations of subject’s reaction times (RT) in SVIPT. Data was collected from three subjects (age 23, 25, 33) using 63 EEG channels plus 1 EOG channel. Sampled at 1 kHz, band-filtered to 0.7–90 Hz and sub-sampled at 500 Hz, the data was cleaned from eye- and muscle artifacts. Oscillatory EEG components, which co-modulate in power with RT, were optimized using the novel SPoC method (Dahne et al., 2014). All results reported were computed following a 5-fold cross-validation procedure. Results For all subjects, SPoC results in alpha sources with focus on parietal-occipital channels. Fig. 1 shows the estimated probability density distributions (pdf) of the true trial-wise RT using only the 33% of fastest and slowest trials, as predicted by the power of pre-trial SPoC sources. To derive the pdfs, RT values estimated using the power of the oscillatory component were sorted. For VP2 and VP3, a slight separation between groups of fast and slow trials is observed, with left-shifted pdfs for predicted faster trials. For data of VP1 a separation is not evident. The mean RT values dependent on the percentage of included quickest trials are plotted in Fig. 2 . An asymptotic trend is present for VP2 and VP3, which indicates, that taking only trials with the predicted shortest RT values in fact lead to shorter average RTs. Conclusion The proposed EEG-tracked SVIPT is a promising tool for the prediction of trial-wise performance. Eventually applied in an online motor rehabilitation scenario, it may be versatile to gate the start of each single trial. In the hands of therapists it may allow to make training trials either harder or easier for the subject. Acknowledgement Funded by DFG, BrainLinks-BrainTools (EXC1086).