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- W4310580007 abstract "With the progressive advancement of neuroscience and engineering research, brain computer interface (BCI) has a strong theoretical foundation and a large development area. On this foundation, various implementation techniques were developed to improve classification precision, equipment stability, and other critical performance metrics. As a result, this work focuses on the power supply, electroneurographic signal capture, feature matching algorithm, and compound feedback modality of a BCI device. Among the many distance modes, an ultrasonic wave with a changing frequency-depth curve best matches the tissue environment. Signal acquisition patterns are a trade-off in complex application scenarios, the method invasiveness is usually along with the signal clearness, if high accuracy control and science researches are emphasized, intracerebral stimulation (ECoG, MEA) is undoubtedly superior to extracranial signal extraction (EEG). While spike neural netwrk (SNN), a form of bio-inspired algorithm, has demonstrated good feature mapping capabilities, it can be very effective for original signal processing. In order to improve the system's performance, a feedback mechanism is incorporated into the closed-loop system, moreover, compared to sole modality feedback, compound feedback revealed superior performance. So, a proposed combination of these methods should bring much improved portability and precision, which might have promising impact on patients with disabilities." @default.
- W4310580007 created "2022-12-12" @default.
- W4310580007 creator A5029031709 @default.
- W4310580007 date "2022-12-02" @default.
- W4310580007 modified "2023-10-14" @default.
- W4310580007 title "A novel closed-loop BCI system based on comparison of multiple methods" @default.
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- W4310580007 doi "https://doi.org/10.1117/12.2641147" @default.
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