EEG-based-emotion-analysis-using-DEAP-dataset-for-Supervised-Machine-Learning

This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning techniques.

• Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

• In the current work, music video clips are used as the visual stimuli to elicit different emotions. To this end, a relatively large set of music video clips was gathered.

• 32 participants took part in the experiment and their EEG and peripheral physiological signals were recorded as they watched the 40 selected music videos.

• Participants rated each video in terms of arousal, valence, like/dislike, dominance and familiarity. For 22 participants, frontal face video was also recorded.

• The database contains all recorded signal data, frontal face video for a subset of the participants and subjective ratings from the participants.

#operation

1)Store the dataset in folder--> data/keep the dataset here.

2)Run the runFile.py file