Abstract
Convolution neural network (CNN) presents high robustness in computer vision applications. In state-of-the-art methods, CNN is being used in EEG processing for various classification and problem solving. To enable EEG to fit in the CNN architecture, data conversion of EEG has to be done. The ways of data conversion need to be investigated in order to fully utilize the information. From the study, it was found that the simplest way of re-arranging the signal is by creating a two dimensional matrix of channels versus time points. There are approaches that compute Pearson correlation coefficients and fit them into a two dimensional matrix to represent the input signal. There are also methods which extract frequency components and fit them in matrix structure as channels versus frequency components, such as power spectral density. Other approaches includes graph representation and wavelet components.
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This research is partly supported by the Ministry of Higher Education (MoHE), Malaysia, via Trans-disciplinary Research Grant Scheme (TRGS) with grant number 203/PELECT/6768002.
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Lai, C.Q., Ibrahim, H., Abdullah, M.Z., Abdullah, J.M., Suandi, S.A., Azman, A. (2019). A Literature Review on Data Conversion Methods on EEG for Convolution Neural Network Applications. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_66
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