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A new Convolutional Neural Network (CNN) architecture to classify nonstationary biomedical signals using their time–frequency representations is proposed. The present method uses the spectrogram of the biomedical signals as an input to CNN, in addition Non-negative matrix factorization (NMF) dictionary elements are used as an additional feature to improve the performance of the CNN model. Considering a number of applications involving eye state classification, such as in Parkinson’s disease detection, analysis of eye fatigue in 3D TVs, driver’s drowsiness detection, infant sleep-waking state identification, and classification of bipolar mood disorder and attention deficit hyperactivity, the proposed method was applied to Electroencephalography (EEG) data for classification of eye state. First, the spectrogram of EEG signal is obtained and used as an image input to CNN, simultaneously, the NMF feature is also …