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...

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Egile Nagusiak:	Noor Faris Ali, Nabil Albastaki, Abdelkader Nasreddine Belkacem, Ibrahim M. Elfadel, Mohamed Atef
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IEEE 2024-01-01
Saila:	IEEE Access
Gaiak:	Depression detection EEG LSTM encoder attention mechanism EEG wearables
Sarrera elektronikoa:	https://ieeexplore.ieee.org/document/10620195/
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