A Low-Complexity Combined Encoder-LSTM-Attention Networks for EEG-based Depression Detection
Despite the high performance of existing state-of-the-art deep learning models for depression detection using electroencephalography (EEG), they incur a heavy computational burden.In this paper, we propose an efficient model consisting of a cascade of an encoder, long short-term memory (LSTM), and attention mechanism networks.The encoder compresses