EEG Bases Emotion Detection Using Deep Learning Algorithm

The use of physiological markers to identify human emotion states is a rapidly emerging area of research in human-computer interaction. Gaming Disorder has become a major topic of concern in recent years, piqueing the curiosity of experts who want to learn more about it. The goal of this study is to use electroencephalography to detect the emotional behaviour of people who play online games on a regular basis (EEG). EEG is a prominent method for investigating addictive behaviours that is low cost and has a good temporal resolution. As a result, the EEG will be a good predictor of the subject’s emotional state. The current study uses the SEED-IV data source to build a model for emotion states, and obtained signals are used to investigate gamming addiction behaviour. The VGG pre-trained model is supplied the spectrogram features. On the SEED-IV database, the trained model has a prediction accuracy of 89.54 percent and a testing accuracy of 78.63 percent. The acquired signals are checked against the trained model, yielding a 75 percent accuracy.

Author(S) Details

S. Thejaswini
Department of Electronics and Telecommunication, B M S Institute of Technology & Management, India.

N. Ramesh Babu
Department of Computer Science Engineering, Amruta Institute of Engineering & Management Sciences, India.

K. M. Ravikumar
Oxford College of Engineering, India.

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