Electroencephalogram signal preprocessing method based on self-adapting noise cancellation system

Abstract

The invention relates to an electroencephalogram signal preprocessing method based on a self-adapting noise cancellation system. In the method, fast independent component analysis (FastICA) and discrete wavelet transform (DWT) are used for estimating electronystagmogram signals in original electroencephalogram signals, wherein estimated electronystagmogram signals are used as reference electronystagmogram input signals of a self-adapting noise cancellation system (ANC); and a self-adapting filter and a weighting coefficient updating module are used for automatically removing electronystagmogram artifacts in the electroencephalogram signals, so that the automatic electronystagmogram signal component recognition, automatic parameter updating, and automatic electronystagmogram signal removalare achieved.

Description

technical field [0001] The invention relates to the field of physiological electroencephalogram signal processing, in particular to a method capable of realizing automatic identification of noise components, automatic updating of parameters and automatic removal of noise signals. Background technique [0002] Electroencephalogram (EEG) is an important bioelectrical signal of the human body, which contains a large amount of biological information of the human body. EEG signals change together with the cognitive process of the brain, enabling rapid responses to external stimuli. By monitoring the nerve electrical signals during the operation, feedback the changes in the integrity of the nerve function during the operation to the surgeon and the anesthesiologist, and find out the cause of the impaired cognitive function in time, so that they can take preventive measures to avoid irreversible damage and reduce the risk of surgery. risk of neurological deficits. [0003] Howeve...

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Problems solved by technology

[0004] At present, the commonly used methods for removing oculograph artifacts are: wavelet transform (WT) and independent component analysis (ICA), etc. WT is a typical time-frequency analysis method, but this method is limited by the wavelet basis function and the number of decomposition layers. The influence of subjective selection, lack of adaptability; independent component analysis (ICA) can effectively remove the local features of the signal, but this method requires a large amount of calculation to solve the separation matrix, and even may not converge, and the real-time performance is not good

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