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Piyush-Bhardwaj / repository
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.
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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