Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gondkar, Raju Ramakrishna; Gondkar, Surekha R.; Sivasakthivel, Ramkumar; Gobinath, R.; Rajagopal, Manikandan Title A name given to the resource A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Engineering Trends and Technology;Volume;74;Issue;1;pp.65-84 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/22315381/IJETT-V74I1P105" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/22315381/IJETT-V74I1P105</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028968323?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028968323?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Attention-Based Bidirectional Gated Recurrent Unit; Convolutional Autoencoder; DEAP; Graph Convolutional Network; SEED Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23490918; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online