Analysis method of brain electricity of forehead area

Abstract

The invention specially relates to an analysis method of brain electricity of a forehead area. By means of testing and sampling, a reference brain electricity signal of the forehead is obtained, and an ocular electricity noise average waveform is extracted from the reference brain electricity signal. Band-pass filtering waves and notching filtering waves are adopted for removing high-frequency noise and power-line interference noise during real-time processing of brain electricity data to obtain a real-time primarily-filtered brain electricity signal of the forehead. According to a preset amplitude threshold value of the ocular electricity noise, blink cycle of the real-time primarily-filtered brain electricity signal of the forehead is extracted, and a method for overlapping the ocular electricity noise average waveform is adopted for counteracting the noise interference caused by blink in the real-time primarily-filtered brain electricity signal of the forehead. According to the analysis method of the brain electricity of the forehead area, purification and noise removal can be conducted on brain electricity data of a single electrode without arrangement of ocular electricity collecting electrodes, the signal-to-noise ratio of the brain electricity signal can be increased, and meanwhile, since a method for extracting a standard ocular electricity waveform in advance is adopted, the time for processing data in the later period is shortened, and the requirement for instantaneity is better met.

Description

technical field [0001] The invention relates to an electroencephalogram analysis method, in particular to an electroencephalogram analysis method in the frontal region. Background technique [0002] Brain-computer interface devices often set electrodes on the frontal area of ​​the human head, and measure people's concentration through feature extraction. However, the frontal EEG signals collected by the leads of the frontal brain area are easily disturbed by eye blinking and mixed with oculoelectric noise, resulting in low signal-to-noise ratio, poor reliability, and difficulty in feature extraction. The removal of oculoelectric noise in the prior art relies on principal component analysis and independent component analysis methods, which require a separate set of oculoelectric collection electrodes located around the eyes, which increases the complexity of the equipment. At the same time, these two denoising methods require more The long duration makes it difficult to meet...

AI Technical Summary

Problems solved by technology

However, the frontal EEG signals collected by the leads of the frontal brain area are easily disturbed by eye blinking and mixed with oculoelectric noise, resulting in low signal-to-noise ratio, poor reliability, and difficulty in feature extraction.

The removal of oculoelectric noise in the prior art relies on principal component analysis and independent component analysis methods, which require a separate set of oculoelectric collection electrodes located around the eyes, which increases the complexity of the equipment. At the same time, these two denoising methods require more Long duration, difficult to meet real-time requirements

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