Artificial Intelligence

    

EEG Emotion Classification Using 3-Dimensional Convolutional Neural Networks

Authors: Maksym Oleksandrovich Stavratii

Classification of electroencephalography (EEG) signals has important applications in the diagnosis and treatment of various neurological disorders. In this paper, we propose a methodology for classifying EEG signals based on signal processing using wavelet transform and superlet transform. The wavelet transform is used to decompose the EEG signal into frequency components, which are then used as features for classification. The proposed approach is evaluated using the publicly available "GAMEEMO" EEG dataset, which has been annotated by valence and emotional arousal. We use a Convolutional Neural Network (CNN) for classification at the waveform level. The results of this study suggest that the wavelet transform and its modifications, such as the superlet transform, can be valuable tools for analyzing and classifying EEG signals

Comments: 7 Pages.

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Submission history

[v1] 2023-06-09 01:04:04

Unique-IP document downloads: 199 times

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