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• W3198553134 endingPage "110116" @default.
• W3198553134 startingPage "110116" @default.
• W3198553134 abstract "As a new type of human–computer interaction technology, brain-computer interface (BCI) systems can translate the electroencephalography (EEG) signals as control commands. In our previous study, the synchronous hybrid mental tasks have been proposed with higher classification accuracy. In this study, to analyze the superiority of synchronous hybrid mental tasks, the causal networks of synchronous hybrid mental tasks and single mental tasks are compared. The causal networks are based on functional connectivity, which is calculated by partial directed coherence. The networks of the synchronous hybrid mental tasks present different interaction patterns. To further improve the classification accuracy of EEG signals, a feature extraction method combining the causal network and common spatial pattern (CSP) is proposed. After combining as mixed feature vectors, they are selected by binary quantum-behaved particle swarm optimization. The optimal feature subsets are classified by extreme learning machine. This average result is 3.1% and 9.5% higher than CSP and causal flow, respectively. The superiority of hybrid experimental paradigm can be studied by the causal network, and the performance of BCIs is also improved." @default.
• W3198553134 created "2021-09-13" @default.
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• W3198553134 date "2021-12-01" @default.
• W3198553134 modified "2023-09-22" @default.
• W3198553134 title "Analysis and application of functional connectivity in synchronic hybrid mental tasks for brain-computer interface" @default.
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• W3198553134 doi "https://doi.org/10.1016/j.measurement.2021.110116" @default.
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