Efficient EEG-based schizophrenia classification via a lightweight hybrid CNN-BiLSTM model.
A novel hybrid deep learning framework that integrates one-dimensional Convolutional Neural Networks (1D-CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to simultaneously learn discriminative spatial features and complex temporal dependencies inherent in EEG signals is proposed.