Parameter optimization method for decision-making model of brain-computer interface system

Abstract

The invention relates to the technical field of brain-computer interfaces, especially a parameter optimization method for a decision-making model of a brain-computer interface system. The method comprises the following steps: (1), collecting brain-computer training data, and carrying out the preprocessing; (2), carrying out data classification through a linear space integrated single detection method, and the positioning of an evidence-accumulating start process. The method employs the linear space integrated single detection method to carry out the classification and recognition of two experiment conditions in training data, carries out the time locating of the evidence-accumulating process in a decision-making model through recognizing the change tendency of the accuracy with time in a single experiment, and carries out the parameter optimization of a sequential decision model. Compared with a previous sequential decision model, the method carries out the locating of the evidence-accumulating process in the decision-making model, eliminates the ineffective classified information accumulation process, carries out the accumulation of the effective classification information in the evidence-accumulating process, and improves the instantaneity of the brain-computer interface system based on the decision-making model.

Description

technical field [0001] The invention relates to a parameter optimization method of a brain-computer interface system decision model, belonging to the technical field of brain-computer interface. Background technique [0002] The brain-computer interface system is a communication system that does not rely on muscle control, and aims to provide a new communication channel for disabled patients or patients with sports injuries. The core of brain-computer interface technology is to identify different states of conscious activity through the classification and processing of EEG signals. The EEG signals recorded from the scalp are very weak and the signal-to-noise ratio is very low, so different pattern recognition methods are applied to BCI systems to extract EEG feature information and train classifiers to minimize the classification error rate. To this end, researchers have conducted extensive research on EEG classification methods, such as support vector machines, artificial ...

AI Technical Summary

Problems solved by technology

The accumulation of decision-making information in the stages of stimulus perception and attention is not beneficial to the classification results, but instead increases the decision-making time of the brain-computer interface system and reduces the real-time performance of the system

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