System and method for controlling brain computer interface (BCI) based on multimode fusion

Abstract

The invention provides a system and method for controlling a brain computer interface (BCI) based on multimode fusion. The system comprises a brain electrostimulation and feedback module, an electroencephalogram (EEG) signal acquisition module, an EEG signal processing module and an execution module, wherein the brain electrostimulation and feedback module is used for evoking an SSVEP (Steady State Visual Evoked Potential) and inducing an MI (Motor Imagery); the EEG signal acquisition module is used for acquiring EEG signals; the EEG signal processing module is used for extracting, identifying and classifying SSVEP characteristics and MI EEG characteristics in the EEG signals and feeding classified results which respectively correspond to the SSVEP characteristics and the MI EEG characteristics back to the brain electrostimulation and feedback module; and the execution module is used for executing the classified results. According to the system and the method, a multimode fusion BCI is constructed, so that the information transmission rate, reliability and flexibility of a control system are improved, the low information transmission rate of a BCI in a single MI mode is reduced, meanwhile, the visual burden under a single SSVEP task is reduced, and the adaptation crowds of the BIC-based control system are increased.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and relates to a brain-computer interface control system and method based on multi-mode fusion. Background technique [0002] Brain Computer Interface (BCI) is a new way of human-computer interaction. It uses advanced machine learning and pattern recognition algorithms to identify the neural activity signals of the brain under different thinking activities, and translate them into control commands for direct control. external devices, thereby establishing direct communication between the human brain and external devices. The research of brain-computer interface is becoming a hot spot in artificial intelligence and rehabilitation engineering, and has received more and more extensive attention. [0003] Existing BCI systems are all based on a single pattern of EEG signals, which can be classified into three categories: the first category is the BCI system based on event-related desynchroniza...

AI Technical Summary

Problems solved by technology

Difficulty restarting even though it can be turned off by the patient

[0008] 2) The environmental adaptability is poor, and the control itself requires a lot of user attention and environmental safety factors

Generally speaking, the biggest problem is poor reliability, and the information transmission rate is far from meeting the actual complex communication requirements

[0009] In addition, the study found that for various modes of brain-computer interface systems, there are about 15%-30% of users who cannot effectively control the brain-computer interface system because they cannot effectively generate the corresponding mode of EEG characteristics.

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