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Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification

Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification

Nagabushanam Perattur, S. Thomas George, D. Raveena Judie Dolly, Radha Subramanyam
Copyright: © 2021 |Volume: 11 |Issue: 1 |Pages: 8
ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799864103|DOI: 10.4018/IJAIML.2021010102
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MLA

Perattur, Nagabushanam, et al. "Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification." IJAIML vol.11, no.1 2021: pp.15-22. http://doi.org/10.4018/IJAIML.2021010102

APA

Perattur, N., George, S. T., Dolly, D. R., & Subramanyam, R. (2021). Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(1), 15-22. http://doi.org/10.4018/IJAIML.2021010102

Chicago

Perattur, Nagabushanam, et al. "Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 11, no.1: 15-22. http://doi.org/10.4018/IJAIML.2021010102

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Abstract

This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.