CBAM-DeepConvNet: Convolutional Block Attention Module-Deep Convolutional Neural Network for asymmetric visual evoked potentials recognition

Purpose: This study aimed to improve the accuracy and the ITR in the stimulative paradigm of character spelling systems based on asymmetric Visual Evoked Potentials (aVEPs) by utilizing EEG signal and an improved Convolutional Block Attention Module-Deep Convolutional Neural Network. Methods: This s...

Descrizione completa

Dettagli Bibliografici

Autori principali:	Zhouyu Ji, Shuran Li, Hongfei Zhang, Chuangquan Chen, Qian Xu, Junhua Li, Hongtao Wang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Taylor & Francis Group 2025-12-01
Serie:	Brain-Apparatus Communication
Soggetti:	Asymmetric visual evoked potentials (aVEPs) Convolutional Block Attention Module (CBAM) Deep Convolutional Neural Network (DeepConvNet)
Accesso online:	https://www.tandfonline.com/doi/10.1080/27706710.2025.2489396
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!