Mehta, Jhanvi H.Jhanvi H.MehtaPriyadarshani, MuskanMuskanPriyadarshaniJha, AditiAditiJhaMiyapuram, Krishna P.Krishna P.Miyapuram2026-01-122026-01-122025-06-25[9798400711244]10.1145/3703323.37037092-s2.0-105012251183http://repository.iitgn.ac.in/handle/IITG2025/33839We designed a CNN-based Multi-task (MTL) classification model to predict music-video clips and identify participants from EEG responses to affective audio-visual stimuli from the DEAP dataset. Leveraging the capabilities of MTL models, participants are identified more accurately and clips are predicted better than single-task techniques. Variable train-test splits do not affect participant prediction task but show depreciation in clip prediction accuracy. It is true for both multi-task and singular task models. These findings show that MTL models may capture complex brain patterns with improved accuracy, making it a reliable EEG-based categorization solution.true1D CNN | Classification | EEG | Multi-task Multi-class | Music Video Clip Prediction | Participant IdentificationConference Proceeding335-33725 June 20250cpConference Paper