Classification of EEG signals on SEED dataset using improved CNN

Conference paper

Ramar, B., Ramalakshmi, R., Gandhi, V. and Pandiselvam, P. 2023. Classification of EEG signals on SEED dataset using improved CNN. 2nd International Conference on Edge Computing and Applications. Namakkal, India 19 - 21 Jul 2023 IEEE. pp. 1095-1102 https://doi.org/10.1109/ICECAA58104.2023.10212279
TypeConference paper
TitleClassification of EEG signals on SEED dataset using improved CNN
AuthorsRamar, B., Ramalakshmi, R., Gandhi, V. and Pandiselvam, P.
Abstract

The proposed research introduces an Improved Convolutional Neural Network (ICNN) to construct EEG-based emotion detection models. This study has utilized an EEG dataset of 15 subjects available from a BCMI laboratory. In our work, differential entropy characteristics obtained from multichannel EEG data are used to train the Improved CNN. The best classification accuracy is 95.67% which is significantly higher than that of the original 62 channels. The most important channels and frequency bands are identified by Improved CNN. The outcomes of our study also demonstrate the existence of neuronal signatures linked to various emotions, which are consistent between sessions and people. Finally, the effectiveness of deep and shallow models are compared and also the performance of improved CNN is compared with benchmark algorithms.

KeywordsBrain Computer Interface (BCI); Electroencephalogram (EEG); Discrete Wavelet Transform (DWT); Convolutional Neural Network (CNN)
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeHealth & Wellbeing
Conference2nd International Conference on Edge Computing and Applications
Page range1095-1102
Proceedings Title2023 2nd International Conference on Edge Computing and Applications (ICECAA)
ISBN
Electronic9798350347579
PublisherIEEE
Publication dates
Print19 Jul 2023
Online16 Aug 2023
Publication process dates
Accepted2023
Deposited22 Jan 2024
Output statusPublished
Accepted author manuscript
File Access Level
Open
Copyright Statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/ICECAA58104.2023.10212279
Web address (URL) of conference proceedingshttp://doi.org/10.1109/ICECAA58104.2023
LanguageEnglish
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