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Boosting brain-computer interface performance through cognitive training: A brain-centric approach

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摘要 Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
出处 《Journal of Information and Intelligence》 2025年第1期19-35,共17页 信息与智能学报(英文)
基金 supported by the National Natural Science Foundation of China (U19B2030, 61976167, 62301405, 62101416) the Natural Science Basic Research Program of Shaanxi, China (2022JQ-708) Fundamental Research Funds for the Central Universities, China.
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