DEEP LEARNING FOR FEATURE CLASSIFICATION OF EEG TO ACCESS STUDENT’S MENTAL STATUS - A. , Syedali Fathima; S., Mythili (International Journal of Psychosocial Rehabilitation, 2020)
Electroencephalography (EEG) analysis has been a significant tool in neuroscience with uses of neural designing and brain-PC interfaces. A large number of the explanatory tools utilized in EEG contemplates have utilized machine learning to reveal significant data for neural classification and neuroimaging. As of late, the accessibility of enormous EEG informational indexes and advances in machine learning have both prompted the arrangement of deep learning structures, particularly in the examination of EEG signals and the usefulness of the cerebrum. In our proposed work, cuttlefish-optimized (CFO) deep neural networks are utilized to distinguish depression in individuals by analyzing EEG signals. The best highlights of alpha, beta, and theta values chosen by the CFO so as to improve accuracy. The execution is assessed utilizing the Database for Emotion Analysis Physiological Signals (DEAP), which is an open EEG dataset. A list of capabilities is removed in 32 EEG channels, which comprises of measurable highlights, Hjorth parameters, band power, and frontal alpha asymmetry. A MATLAB tool has been utilized to assess the execution of the proposed framework.