Brain-computer interface signal processing methods and brain-computer interface systems

Abstract

A brain-computer interface (BCI) signal processing method and system are disclosed. The method includes: receiving brain activity detection signals from a target object; mapping a parameter matrix G to an in-memory computing array, wherein a first matrix H, a second matrix W, and a third matrix T are obtained based on a training model including multiple stimulus task templates to perform temporal filtering, spatial filtering, and template matching on the brain activity detection signals; calculating a parameter matrix G, G = TWH, for one-step decoding of the brain activity detection signals; using the in-memory computing array mapping the parameter matrix G to perform one-step decoding of the brain activity detection signals to obtain a decoding result corresponding to the brain activity detection signals; determining, based on the decoding result, a control command for the target stimulus task corresponding to the brain activity detection signals from multiple stimulus task templates; and sending the control command to execute the task. This method reduces the computational load and latency of the algorithm, and decreases circuit area and energy consumption.

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