Abstract
Brain-computer Interfaces aims to assess brain activity patterns by analyzing multichannel time series extracted from electrical recordings, as a result of neuron interactions, e.g., Electroencephalography (EEG) that is a record of the neuronal electrical activity measured in the cerebral cortex having a high temporal resolution. Generally, BCI systems are based on the cognitive neuroscience paradigm termed as Motor Imagery (brain activity patterns of the imagination of a motor action, e.g., the imagination of hand movements). Nevertheless, the designing an MI-based BCI system requires an appropriate EEG data analysis to reach the needed performance for real-world BCI applications. Particularly, the selection of the active segment or the segment with the informative signals related to a determinate MI task is determinant for the possible performance. Hence, to select the window signal stimulus-related, detecting temporal changes in data are necessary to understand how a cognitive process unfolds in response to a stimulus. For this purpose, a non-stationary degree estimation based on the first Statistical moments (mean and covariance) is assessed. The results show that the changes in the non-stationary measure are directly related to executed stimulus during the EEG MI recording. The findings could be used to select the active analysis window and consequently, improving the MI classification tasks.
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Acknowledgment
This work was supported by Under grants provided by a PhD. scholarship funded by COLCIENCIAS (covocatoria No. 727) and the project Sistema de realidad aumentada para la proyeccin de conexiones cerebrales producidas por estmulos afectivos a partir de seales de EEG, (cdigo 36075).
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Velasquez-Martinez, L.F., Alvarez-Meza, A., Castellanos-Dominguez, G. (2017). Detection of EEG Dynamic Changes Due to Stimulus-Related Activity in Motor Imagery Recordings. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_43
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