ItemArticle

A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals

Title
A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals
Creator
Gondkar, Raju Ramakrishna; Gondkar, Surekha R.; Sivasakthivel, Ramkumar; Gobinath, R.; Rajagopal, Manikandan
Description
Electroencephalography-based Brain-Computer Interfacing (EEG-BCI) technologies allow for effortless interaction between external hardware and the human brain through monitoring its electric signals. These systems rely on EEG recordings, which provide non-invasive and real-time neural information through electrodes placed on the scalp. To advance emotion-recognizing efficiency and accuracy, this study proposes a deep learning-based method that can extract valuable temporal and spatial information from EEG signals. The proposed model includes the use of a Graph Convolution Network (GCN) for learning spatial relationships between different EEG channels to model the data in graph form and gain features through that modelling. A Convolutional Autoencoder (CAE) is then used to compress data to low dimensions and to reconstruct it so that major features are not ignored. Furthermore, the model uses an Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) for temporal classification, which can emphasize the most important time steps in both backwards and forward directions. Two standard datasets are employed to test the developed approach. The DEAP dataset is used for emotion recognition with a binary response, and SEED is used with multi-class classification. The model attains great results of 98.12% accuracy on DEAP and 97.58% on SEED datasets. The very high performances show the efficacy of the model for decoding emotional states from EEG signals and very strong potential for real-time emotion recognition in affective computing and BCI. 2026 Seventh Sense Research Group.
Source
International Journal of Engineering Trends and Technology;Volume;74;Issue;1;pp.65-84
Date
01-01-2026
Publisher
Seventh Sense Research Group
Subject
Attention-Based Bidirectional Gated Recurrent Unit; Convolutional Autoencoder; DEAP; Graph Convolutional Network; SEED
Coverage
Gondkar R.R., Christ (Deemed to be University), Karnataka, Bangalore, India; Gondkar S.R., Department of Electronics and Communication, BMS Information Technology and Management, Karnataka, Bangalore, India; Sivasakthivel R., Christ (Deemed to be University), Karnataka, Bangalore, India; Gobinath R., Christ (Deemed to be University), Karnataka, Bangalore, India; Rajagopal M., Christ (Deemed to be University), Karnataka, Bangalore, India
Rights
All Open Access; Bronze Open Access
Relation
ISSN: 23490918;
Format
online
Language
English
Type
Article

Citation

Gondkar, Raju Ramakrishna; Gondkar, Surekha R.; Sivasakthivel, Ramkumar; Gobinath, R.; Rajagopal, Manikandan, “A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/23258.

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