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- W4313216519 abstract "Intracortical brain-computer interface (iBCI) systems have evolved into a clinical approach to assist people with disabilities and paralysis. The decoder is one of the most crucial parts of iBCI systems. In reality, it is difficult to maintain efficient performance with a fixed decoder due to frequent changes in the distribution of data recorded over time. To improve the reliability of iBCI systems, usually, it is necessary to recalibrate decoders every time with a large amount of current data. However, it is difficult to obtain enough current data for recalibration. To reduce the recalibration data, we propose a domain adaptation-based decoder calibration framework (DA-DCF) for decoding reach-and grasp movements, which utilizes a small amount of current data and robustly achieves good performance. In the DA-DCF, a source convolutional neural network (CNN) learned representation from source data to initialize the target CNN. Secondly, the target CNN was trained through a confusing domain discriminator until it would not distinguish between the source and target domains. Thirdly, the features transformed by the target CNN were classified by the classifier for movements in the target domain. Finally, the practicality and efficiency of the DA-DCF were evaluated with other comparative frameworks. The comparative experiments were conducted on the data from a male rhesus monkey, which was trained to perform three categories of reach-and-grasp tasks across two sessions. Our results showed that DA-DCF diminished the disparity between source and target data, and significantly reduced the amount of recalibration data while improving the decoding accuracy. It is plausible to utilize DA-DCF in real-time iBCI systems of the forelimb movement control." @default.
- W4313216519 created "2023-01-06" @default.
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- W4313216519 date "2023-03-01" @default.
- W4313216519 modified "2023-09-26" @default.
- W4313216519 title "Decoder calibration framework for intracortical brain-computer interface system via domain adaptation" @default.
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- W4313216519 doi "https://doi.org/10.1016/j.bspc.2022.104453" @default.
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