An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features

• Published in: Frontiers in neuroscience Vol. 17; p. 1194554
• Main Authors: Chen, Di, Huang, Haiyun, Bao, Xiaoyu, Pan, Jiahui, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 12.07.2023; Frontiers Media S.A
• Subjects: Accuracy; Attention; attention recognition; Brain research; brain-computer interfaces (BCIs); Classification; Cognitive ability; Cognitive load; EEG; electroencephalogram (EEG); Electroencephalography; Experiments; inter-subject; intra-subject; Memory; Neuroscience; Nonlinear systems; Physiology; Research methodology; valid paradigm; valid paradigm; brain-computer interfaces (BCIs); intra-subject; attention recognition; inter-subject; electroencephalogram (EEG); neural patterns
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1194554

Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects. In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects. We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, β and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.