decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decodingmodels are built under the attended state while subjects perfo...
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decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decodingmodels are built under the attended state while subjects perform motion tasks. In practice, people are often distracted by other tasks or environmental factors, which may impair decoding performance. To address this problem, in this paper, we propose a hierarchical decoding model of human upper limb motion intention from EEG signals based on attention state estimation. The proposed decodingmodel includes two components. First, the attention state detection (ASD) component estimates the attention state during the upper limb movement. Next, the motion intention recognition (MIR) component decodes the motion intention by using the decodingmodels built under the attended and distracted states. The experimental results show that the proposed hierarchical decoding model performs well under the attended and distracted states. This work can advance the application of human movement intention decoding and provides new insights into the study of brain-machine interfaces.
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