Self-adaptive electroencephalogram filtering method

Abstract

Provided is a self-adaptive electroencephalogram filtering method. The device comprises a signal acquisition unit, an analog signal processing unit and a self-adaptive filtering unit, wherein the signal acquisition unit is used for acquiring user scalp electroencephalogram signals, spatial electromagnetic signals and electrode motion signals and producing electroencephalogram voltage signals U1, spatial electromagnetic voltage signals U2 and electrode motion digit signals D3. The analog signal processing unit is used for further amplifying the acquired voltage signals U1 and the voltage signals U2 to millivolt level, conducting filtering and AD conversion processing on the U1 and U2, respectively generating the digit signals D1 and D2 and sending the digit signals and the D3 to the self-adaptive filtering unit. The self-adaptive filtering unit utilizes the spatial electromagnetic signals D2 and the electrode motion signals D3 obtained through processing of the analog signal processing unit and adopts a common mode reference method and a blind signal separation algorithm to conduct filtering processing on the electroencephalogram signals D1. The interference of electromagnetic radiation and motion artifacts in the electroencephalogram signal acquisition process can be effectively relieved, and the signal-to-noise ratio of the electroencephalogram signals is improved.

Description

technical field [0001] The invention mainly relates to the technical field of brain-computer interface and medical equipment, in particular to an EEG self-adaptive filtering method based on common-mode brain electrodes and MEMS motion sensor input signals. Background technique [0002] Brain-computer interface technology is an advanced human brain that does not require the participation of peripheral nerves and muscles, directly recognizes human intentions by detecting brain neural activity, and converts them into computer control instructions, thereby realizing the operation and control of external equipment by the human brain. Machine control and interaction technology. The original intention of the brain-computer interface technology research is to restore the ability to control and interact with the external environment for patients with severe motor dysfunction, to help disabled people perform assisted sports and sports rehabilitation, and improve their quality of life....

AI Technical Summary

Problems solved by technology

However, due to the high strength and complex components of the noise signal caused by user movement and space electromagnetic radiation, it is difficult to effectively improve the signal-to-noise ratio of EEG signals based on this filtering method alone, and it is urgent to develop new filtering techniques to remove the above-mentioned artifacts. interference

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