DocumentCode :
1556338
Title :
Pre-Stimulus Sensorimotor Rhythms Influence Brain–Computer Interface Classification Performance
Author :
Maeder, Cecilia L. ; Sannelli, Claudia ; Haufe, Stefan ; Blankertz, Benjamin
Author_Institution :
Machine Learning Lab., Berlin Inst. of Technol., Berlin, Germany
Volume :
20
Issue :
5
fYear :
2012
Firstpage :
653
Lastpage :
662
Abstract :
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.
Keywords :
brain-computer interfaces; electroencephalography; medical signal processing; neurophysiology; signal classification; BCI classification performance; LDA; SMR-BCI experiments; brain activity patterns; brain-computer interface; common spatial patterns preprocessing; left hand motor imagery; linear discriminant analysis; parieto-occipital area; post stimulus task performance; prestimulus SMR activity; prestimulus brain activity; prestimulus sensorimotor rhythms; right hand motor imagery; sensorimotor areas; sensorimotor rhythm modulations; task execution quality; Accuracy; Analysis of variance; Brain; Brain computer interfaces; Calibration; Electroencephalography; Visualization; Brain–computer interface (BCI); classification; electroencephalography (EEG); motor imagery; pre-stimulus; sensorimotor rhythms; Adult; Biological Clocks; Brain-Computer Interfaces; Electroencephalography; Evoked Potentials, Somatosensory; Female; Humans; Male; Pattern Recognition, Automated; Periodicity; Reproducibility of Results; Sensitivity and Specificity; Somatosensory Cortex;
fLanguage :
English
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1534-4320
Type :
jour
DOI :
10.1109/TNSRE.2012.2205707
Filename :
6237532
Link To Document :
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