This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxation, concentration, or neutral states. Using publicly available EEG data from a Muse Electroencephalography headband, a large number of features describing the wave are extracted, and subsequently reduced to 256 based on the Information Gain measure. These 256 features are then normalised and reshaped into a 16 × 16 grid, which can be expressed as a grayscale image. A deep Convolutional Neural Network is then trained on this data in order to classify the mental state of subjects. The proposed method obtained an out-of-sample classification accuracy of 89.38%, which is competitive with the 87.16% of the current best method from a previous work.
CITATION STYLE
Ashford, J., Bird, J. J., Campelo, F., & Faria, D. R. (2020). Classification of EEG Signals Based on Image Representation of Statistical Features. In Advances in Intelligent Systems and Computing (Vol. 1043, pp. 449–460). Springer Verlag. https://doi.org/10.1007/978-3-030-29933-0_37
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