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Pre-Stimulus Sensorimotor Rhythms Influence Brain–Computer Interface Classification Performance

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4 Author(s)
Maeder, C.L. ; Machine Learning Lab., Berlin Inst. of Technol., Berlin, Germany ; Sannelli, C. ; Haufe, S. ; Blankertz, B.

The influence of pre-stimulus ongoing brain activity on post-stimulus task performance has recently been analyzed in several studies. While pre-stimulus activity in the parieto-occipital area has been exhaustively investigated with congruent results, less is known about the sensorimotor areas, for which studies reported inconsistent findings. In this work, the topic is addressed in a brain-computer interface (BCI) setting based on modulations of sensorimotor rhythms (SMR). The goal is to assess whether and how pre-stimulus SMR activity influences the successive task execution quality and consequently the classification performance. Grand average data of 23 participants performing right and left hand motor imagery were analyzed. Trials were separated into two groups depending on the SMR amplitude in the 1000 ms interval preceding the cue, and classification by common spatial patterns (CSPs) preprocessing and linear discriminant analysis (LDA) was carried out in the post-stimulus time interval, i.e., during the task execution. The correlation between trial group and classification performance was assessed by an analysis of variance. As a result of this analysis, trials with higher SMR amplitude in the 1000 ms interval preceding the cue yielded significantly better classification performance than trials with lower amplitude. A further investigation of brain activity patterns revealed that this increase in accuracy is mainly due to the persistence of a higher SMR amplitude over the ipsilateral hemisphere. Our findings support the idea that exploiting information about the ongoing SMR might be the key to boosting performance in future SMR-BCI experiments and motor related tasks in general.

Published in:

Neural Systems and Rehabilitation Engineering, IEEE Transactions on  (Volume:20 ,  Issue: 5 )