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Creators/Authors contains: "Ghandi, Mona"

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  1. ; ; (, 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE))
    Emotion detection using machine learning and data gathered from an electroencephalogram (EEG) holds the potential for architecture and creating smart adaptive spaces which can respond to the user's current emotional state detected from the Neurophysiological data in real-time. This technology can help people with mental and physical disabilities to have a greater role in shaping their environment and live more independent lives. In this paper, two different machine learning approaches, the Long Short Term memory network, (LSTM) and Convolutional Neural Network (CNN) are compared in order to assess their potential to satisfy this goal of emotion detection. The LSTM network was trained on eight-channel time-series data which had undergone a Fast Fourier Transform, and the CNN was trained on the un-transformed data in the form of a unique plot-image based approach. 
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