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Brain Signal Classification Based on Deep CNN

Brain Signal Classification Based on Deep CNN

Terry Gao, Grace Ying Wang
Copyright: © 2020 |Volume: 12 |Issue: 2 |Pages: 13
ISSN: 2643-7937|EISSN: 2643-7945|EISBN13: 9781522598909|DOI: 10.4018/IJSPPC.2020040102
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MLA

Gao, Terry, and Grace Ying Wang. "Brain Signal Classification Based on Deep CNN." IJSPPC vol.12, no.2 2020: pp.17-29. http://doi.org/10.4018/IJSPPC.2020040102

APA

Gao, T. & Wang, G. Y. (2020). Brain Signal Classification Based on Deep CNN. International Journal of Security and Privacy in Pervasive Computing (IJSPPC), 12(2), 17-29. http://doi.org/10.4018/IJSPPC.2020040102

Chicago

Gao, Terry, and Grace Ying Wang. "Brain Signal Classification Based on Deep CNN," International Journal of Security and Privacy in Pervasive Computing (IJSPPC) 12, no.2: 17-29. http://doi.org/10.4018/IJSPPC.2020040102

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Abstract

It is essential to increase the accuracy and robustness of classification of brain data, including EEG, in order to facilitate a direct communication between the human brain and computerized devices. Different machine learning approaches, such as support vector machine (SVM), neural network, and linear discrimination analysis (LDA), have been applied to set up automatic subjective-classifier, and the findings for their capacities in this regard have been inconclusive. The present study developed an effective classifier for human mental status using deep learning in a convolutional neural network. In contrast to most previous studies commonly using EEG waveform or numeric value of brain signals for classification, the authors utilised imaging features generated from EEG data at alpha frequency band. A new model proposed in this study provides a simple and computationally efficient approach to distinguish mental status during resting. With training, this model could predict new 2D EEG images with above 90% accuracy, while traditional machine learning techniques failed to achieve this accuracy.

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