International Journal of Technology and Applied Science

E-ISSN: 2230-9004   •   Impact Factor: 9.914

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 9 (September 2026) Submit your research before the last 3 days of this month to publish your research paper in the current issue.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 9
September 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Powered Emotion Detection from EEG Signals

Author(s) Mr. Naveen Saini
Country India
Abstract Emotion recognition from brain activity has emerged as an important research area in affective computing, with electroencephalography (EEG) providing a direct physiological signal for identifying emotional states. This study presents a controlled experimental comparison of three deep learning architectures—one-dimensional Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—for classifying EEG-derived features into negative, neutral, and positive emotional states. A publicly available EEG Brainwave dataset containing 2,132 samples recorded using a 14-channel Emotiv EPOC headset was used for experimentation. The same preprocessing procedure, data split, training configuration, and evaluation criteria were applied to all three architectures to provide a consistent basis for comparison. The models were evaluated using accuracy, precision, recall, F1-score, and confusion-matrix analysis. Experimental results show that the GRU achieved the highest test accuracy of 98.1%, followed by LSTM with 96.2% and CNN with 95.1%. The GRU also achieved the highest macro-averaged precision, recall, and F1-score, demonstrating its effectiveness in capturing sequential patterns within EEG-derived features. The findings indicate that gated recurrent architectures can provide strong classification performance while maintaining relatively lower architectural complexity than graph- and Transformer-based approaches. The study highlights the potential of GRU-based models for resource-efficient EEG emotion-recognition systems and provides a controlled benchmark for comparing commonly used deep learning architectures.
Keywords EEG, Emotion Recognition, Deep Learning, CNN, LSTM, GRU, Affective Computing, Brainwave Signals, Comparative Study.
Field Engineering
Published In Volume 17, Issue 9, September 2026
Published On 2026-09-29
DOI https://doi.org/10.71097/IJTAS.v17.i9.1430
View / Download PDF File

Share this



CrossRef

Crossref DOI prefix of IJTAS is 10.71097/IJTAS

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 17 Isu 9Cover Page Vol 17 Isu 8Cover Page Vol 17 Isu 7

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJTAS
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics 		Join as a Reviewer
Editors & Reviewers
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijtas.com