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 algorithms such as Support vector machine and K - Nearest Neighbor.

# 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

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

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