Brain-computer interface method based on multi-modal signal identification

Abstract

The invention provides a brain-computer interface method based on multi-modal signal identification, and relates to the technical field of brain-computer interfaces. The brain-computer interface method based on multi-modal signal identification comprises the following steps: S1, starting a device to collect brain wave signals; s2, analyzing the brain wave signals and generating corresponding action consciousness; s3, a display screen displays whether the action awareness starts an interface or not; s4, the user selects whether action awareness is started or not through sight line concentration, if yes, the next step is executed, and if not, an action awareness command is not executed; s5, the display screen enters a detailed selection interface, and the user determines the motion consciousness by centralizing on the specific selection interface; s6, when the user enters a motion awareness detailed selection interface, the motion awareness is finally confirmed by analyzing the heart rate fluctuation condition of the user. Through the method designed by the invention, the brain wave signals of the user can be accurately understood through three modes, meanwhile, error signals are not generated, and the practicability is extremely high.

Description

technical field [0001] The invention relates to the technical field of brain-computer interface methods, in particular to a brain-computer interface method based on multimodal signal recognition. Background technique [0002] The brain-computer interface is a kind of information exchange and control between the human brain and computers or other communication devices by collecting and analyzing the signals of the cerebral cortex, and converting them into instructions to control peripheral equipment. It does not depend on the conventional human brain. Normal output channel. Using the human brain to produce different responses to different things or cognitive activities, so as to obtain different types of EEG signals. The conversion of control instructions is realized by amplifying, filtering, collecting, feature extraction, and classification of EEG signals. Currently, there are two methods of EEG acquisition, implantable and non-implantable. The former needs to implant el...

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Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a brain-computer interface method based on multimodal signal recognition, which solves the problem that the current multimodal brain-electrical interface technology mainly relies on visual aids, and it is easy to generate wrong commands in the idle state. The problem

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