Hybrid CNN–LSTM network for cross-subject EEG emotion recognition

Abstract Detecting human emotions from brain signals is fundamental for building intuitive and adaptive human–computer interfaces (HCI); However, Electroencephalography (EEG) remains challenging to interpret because the signals are non-stationary, high-dimensional, and noisy. This study evaluated wh...

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書誌詳細

主要な著者:	Hashim Ali, Zangar Ermaganbet, Muhammad Tahir Akhtar
フォーマット:	Artigo
言語:	Inglês
出版事項:	Springer 2026-05-01
シリーズ:	Discover Artificial Intelligence
主題:	Emotion recognition Hybrid architecture Convolutional block attention module Attention-guided deep learning Cross-subject classification Affective computing
オンライン･アクセス:	https://doi.org/10.1007/s44163-026-01314-z
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