Brain neural signal parallel decoding method and device capable of being dynamically recombined

Abstract

The invention discloses a brain neural signal parallel decoding device and method capable of being dynamically recombined. The method comprises the steps that an interaction control platform issues a control command, configures parameters and receives and calculates a brain neural signal decoding result fed back by a master control subsystem; the calculation main control subsystem analyzes the brain neural signal according to the control command and the configuration parameters, generates a decoding task according to an analysis result, and dynamically distributes the decoding task to the parallel computing subsystem according to the load condition of the parallel computing subsystem; a brain neural signal decoding result fed back by the parallel computing subsystem is received; the parallel computing subsystem is dynamically recombined to the computing master control subsystem, a decoding algorithm chain matched with the analysis task is generated according to the decoding task, the decoding task is executed through the decoding algorithm chain, and a brain neural signal decoding result is obtained; and the neural feedback device converts a brain neural signal decoding result obtained from the calculation main control subsystem into a stimulation signal and feeds back the stimulation signal to an acquisition object, so that high-performance parallel decoding of the brain neural signal is realized.

Description

technical field [0001] The invention relates to the field of brain-computer interface, and specifically relates to a method and device for parallel decoding of brain nerve signals that can be dynamically reorganized. Background technique [0002] Brain-computer interface technology is a bridge between brain science and information science research, and the signal analysis and output control of brain nerve signals are an indispensable part of brain-computer interface technology. However, the brain nerve signal decoding and control feedback systems currently on the market generally have the following problems: [0003] (1) The types of EEG signals that can be decoded by a single system are single, and it is impossible to use the same system to process multiple different brain nerve signals at the same time; (2) The ability to decode brain nerve signals in a single system is limited, and there is a performance bottleneck of the maximum number of channels of nerve signals ; (3)...

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Problems solved by technology

[0003] (1) The types of EEG signals that can be decoded by a single system are single, and it is impossible to use the same system to process multiple different brain nerve signals at the same time; (2) The ability to decode brain nerve signals of a single system is limited, and there is a performance bottleneck of the maximum number of channels of nerve signals ; (3) It is difficult to update and iterate the decoding system, and it is impossible to dynamically upgrade the decoding algorithm library; (4) The decoding configuration is cumbersome, and a large number of parameters need to be modified to switch signal data sources or modify signal data types; (5) The decoding algorithm has poor flexibility and cannot be based on Specific requirements flexibly scale the decoding process

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