Smart empathetic speaker based on real-time EEG-based music therapy

During COVID-19 outbreak, many people are suffering the negative emotions, causing anxiety, fear, and depression in daily life. To improve the individuals’ mental health, musical therapy will be employed due to its high value of treatment in the mental health field. In our project, a smart empathetic speaker based on real-time emotion recognition system will be developed. Through electroencephalography (EEG) measurement, the types of signals reflecting the listener’s emotion will be extracted. The deep learning method will be used to recognize the emotions of the user in real-time. By continuous and extensive deep learning process, the emotion recognition system is expected to become more intelligent, and a personalized database of popularly used songs in music therapy will be organized. Finally, the speaker can assign known music to a listener based on existing music therapy experience and adjust the music according to the feedback obtained from the brain signal sensors.

Intern: 
Junfeng Xiao
Faculty Supervisor: 
Anthony G Straatman;Jun Yang
Province: 
Ontario
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