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Early detection of individuals susceptible to Schizophrenia (SZ) is critical for early intervention, which can reduce the risk of psychosis. This research proposes a deep learning method for classifying EEG data by picking important discriminative EEG features. While existing systems employ an R-CNN methodology, we propose a hybrid CNN–Bi- LSTM automated system that analyses EEG statistical data and performs the prediction. It uses a CNN for an optimised feature selection process to select the most important and informative features, with Bi-LSTM for prediction of susceptibility to develop SZ. The model when run on EEG data of schizophrenic paradigms gives output as ”Schizophrenic” or ”Non-schizophrenic”. This method has a high level of classification accuracy when compared to most existing machine learning models. While it displays a lower accuracy than some complex deep learning systems, it is much more stable and easy to interpret and thus is more practical for clinical settings.

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