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Prediction of voluntary movements from electroencephalographic (EEG) signals is widely used and investigated  for applications like brain-computer interfaces (BCIs) or in the field of rehabilitation. Different combinations  of signal processing and machine learning methods can be found in literature for solving this task.  Machine learning algorithms suffer from small signal-to-noise ratios and non-stationarity of EEG signals. Due  to the non-stationarity, prediction performance of a fixed classifier may degrade over time. This is because  the shape of motor-related cortical potentials associated with movement prediction change over time and thus  may no longer be well represented by the classifier. A solution is online calibration of the classifier. Therefore,  we propose a novel approach in which movement onsets, detected by the analysis of electromyographic  (EMG) signals are used to recalibrate the classifier during runtime. We conducted experiments with 8 subjects  performing self-initiated, self-paced movements of the right arm. We investigated the differences of online  calibration versus applying a fixed classifier. Further the effect of varying initial training instances ( 1  3 or 2  3 of  available data) was examined. In both cases we found a significant improvement in prediction performance  (p < 0:05) when the online calibration was used.

Runtime Calibration of Online EEG based Movement Prediction using EMG Signals