Abstract
This work presents a study that evaluates different scenarios of preprocessing and processing of EEG registers, with the aim to predict fist imaginary movements utilizing the data of the EEG Motor Movement/Imaginary Dataset. Three types of imaginary fist movements have been decoded: sustained opening and closing of right fist, sustained opening and closing of left fist and rest. Initially, the registers were band-pass filtered to separate frequency ranges given by mu rhythms (7.5-12.5 Hz), beta rhythms (12.5-30 Hz), mu&beta rhythms, and a broad range of 0.5-30 Hz. Afterward, the signals of the separated subbands were epoched in time windows of 0-0.5, 0-1, 0-1.5 and 0-2 seconds, as well as preprocessed with two techniques of spatial filtering: common spatial patterns and independent component analysis. In both cases, a set of selected channels was established for feature extraction, by calculation of the logarithms of the variance in the time series corresponding to each preprocessed and selected channel. The classification stage was based on linear discriminant analysis and support vector machines. The results showed that the combination given by common spatial patterns and support vector machines allowed to reach a mean decoding accuracy close to 99.9%, where epoching and filtering to separate subbands did not influence the results in a noticeable way.
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Boelts, J., Cerquera, A., Ruiz-Olaya, A.F. (2015). Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_16
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DOI: https://doi.org/10.1007/978-3-319-18914-7_16
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