International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

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Author: search.filters.author.Nawarathne, MSubject: search.filters.subject.Emotion Recognition

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    Unveiling EEG Emotional Patterns during Interactive Engagement Activities: A Performance Comparison of Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayake, T; Subasinghe, S; Nawarathne, M; Sumathipala, P
    This paper presents a comparative evaluation of machine learning and deep learning models for emotion recognition from electroencephalography (EEG) signals recorded during interactive engagement activities. EEG data were collected using a Muse headband as participants performed engagement activities designed to elicit five emotions: Afraid, Happy, Calm, Neutral, and Sensitive. After preprocessing and feature extraction, eight machine learning and deep learning models were trained. Based on the experiment, Random Forest emerged as the best-performing classifier, achieving ~95.6% accuracy with balanced precision-recall, while gradient boosting and SVM also shown comparative results. The models demonstrated potential for real-time streaming despite slightly lower accuracy, highlighting scalability with larger datasets. These findings demonstrate the feasibility of consumer-grade EEG devices, enhanced with multimodal features, for robust emotion recognition in realistic engagement activities. The results underscore the effectiveness of multimodal fusion and engagement-based design, confirming that lightweight EEG systems can support practical, real-time affective computing applications in education, adaptive interfaces, and mental well-being monitoring.