Sensors and Decoding for Intracortical Brain Computer Interfaces
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Classification for Intracortical Brain-Computer Interfaces Semi-Supervised Classification for Intracortical Brain-Computer Interfaces
Intracortical brain-computer interface (BCI) systems may one day allow paralyzed patients to interface with robotic arms or computer programs using their thoughts alone. However, a common and unaddressed issue with these systems is that due to small instabilities in the recorded signals, the decoding algorithms they rely upon must be retrained daily in a supervised manner. While this may be acc...
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OBJECTIVE Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). APPROACH We r...
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Intracortical brain-computer interfaces (iBCIs) have allowed people with tetraplegia to control a computer cursor by imagining the movement of their paralyzed arm or hand. State-of-the-art decoders deployed in human iBCIs are derived from a Kalman filter that assumes Markov dynamics on the angle of intended movement, and a unimodal dependence on intended angle for each channel of neural activit...
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ژورنال
عنوان ژورنال: Annual Review of Biomedical Engineering
سال: 2013
ISSN: 1523-9829,1545-4274
DOI: 10.1146/annurev-bioeng-071910-124640