P300 single extraction technique based on independent component analysis and Kalman smoothing

Abstract

The invention relates to a P300 single extraction technique based on independent component analysis and Kalman smoothing. The invention aims at improving efficiency and accuracy of a dynamic system based on P300. According to the P300 single extraction technique provided by the invention, by removing winking artifacts and random noise in electroencephalogram signals (EEG), a signal-to-noise ratioof the electroencephalogram signals (EEG) can be improved, so that the electroencephalogram signals can be recognized more easily; with time domain characteristics of the processed electroencephalogram signals (EEG) as classifying characteristics, a characteristic vector can be obtained; the characteristic vector undergoes dimension reduction and mode recognition, so as to judge whether the current electroencephalogram signals (EEG) belong to a P300 potential, and corresponding commands are outputted. The invention is significant for improving the efficiency and accuracy of the dynamic systembased on the P300. The invention belongs to comprehensive application of human-computer interaction science and cognitive neuroscience fields.

Description

technical field [0001] The present invention relates to a P300 single extraction technique based on independent component analysis and Kalman smoothing. Specifically, by processing the EEG signal, it improves its signal-to-noise ratio to make it easy to distinguish, and uses principal component analysis and support vector machine to carry out feature dimension reduction and discrimination separation. The method proposed by the present invention does not require any form of body movement and language, but only requires the user to respond to visual stimuli, and obtains the user's command intention by analyzing the user's EEG signal to realize the command output. The invention belongs to the comprehensive application of the fields of human-computer interaction science and cognitive neuroscience. Background technique [0002] Brain-computer interface (BMI) can establish a direct control channel between the human brain and external devices by converting brain signals into contr...

AI Technical Summary

Problems solved by technology

The eye blink artifact is a noise signal that does not conform to the Gaussian distribution and the frequency spectrum is aliased with the P300 potential. The existing simple filtering method cannot effectively remove the eye blink artifact.

Due to the inability to effectively remove the noise in the EEG signal, the accuracy of a single extraction of the P300 signal is very low

[0005] In summary, in the prior art, there is still a lack of effective solutions for how to effectively suppress random noise while removing eye blink artifacts, and how to improve the accuracy of single-shot extraction of P300 potentials

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