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

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...

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Bibliographic Details
Main Authors: Zhouyu Ji, Shuran Li, Hongfei Zhang, Chuangquan Chen, Qian Xu, Junhua Li, Hongtao Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Brain-Apparatus Communication
Subjects:
Asymmetric visual evoked potentials (aVEPs)
Convolutional Block Attention Module (CBAM)
Deep Convolutional Neural Network (DeepConvNet)
Online Access:https://www.tandfonline.com/doi/10.1080/27706710.2025.2489396
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