Abstract
Human brain uses a complex electro-chemical signaling pattern that creates our imagination, memory and self-consciousness. It is said that Electroencephalography better known as EEG contains signatures of various tasks that we perform. In this paper we study the possibility of categorizing tasks conducted by humans from EEG recordings. The novelty of this study mainly lies in the use of very cost effective consumer grade wireless EEG devices. Three cognitive tasks were considered: text reading and writing, Math problem solving and watching videos. Twelve subjects were used in this experiment. Initial features were calculated from Discrete Wavelet Transform (DWT) of raw EEG signals. After application of appropriate dimensionality reduction, Support Vector Machine (SVM) was used for classification of tasks. DWT + Kernel PCA with SVM based classifier showed 86.09 % accuracy.
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Paul, S.K., Zulkar Nine, M.S.Q., Hasan, M., Amin, M.A. (2015). Cognitive Task Classificaiton from Wireless EEG. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_2
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DOI: https://doi.org/10.1007/978-3-319-23344-4_2
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