An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features - Chen, Di; Huang, Haiyun; Bao, Xiaoyu; Pan, Jiahui; Li, Yuanqing (Frontiers in Neuroscience, 2023)
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. We then constructed an attention recognition framework for 85 subjects and 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.