SSVEP multi-scale noise transfer and characteristic frequency detection method based on FHN-CCA fusion

Abstract

An SSVEP multi-scale noise transfer and characteristic frequency detection method based on FHN-CCA fusion comprises the steps that firstly, multi-channel SSVEP data collection is carried out, then an optimal channel X is determined through a pre-experiment and serves as an output signal of a formal experiment, then signal preprocessing is carried out, useless data in the first 0.1 s are cut off, and useless noise is filtered out through a Butterworth filter; then FHN model parameter initialization and FHN model processing are carried out, preprocessed signals and noise are sent to an FHN model for stochastic resonance processing, target frequency is identified based on a CCA method of spatial filtering and template matching, and finally frequency matching detection is carried out; according to the method, high-precision identification of the characteristic frequency is realized, and the identification accuracy and the information transmission rate of the SSVEP target frequency are greatly increased.

Description

technical field [0001] The invention relates to the technical fields of neural engineering and brain-computer interface in biomedical engineering, in particular to an SSVEP multi-scale noise transfer and characteristic frequency detection method based on FHN-CCA fusion. Background technique [0002] Brain-computer interface technology, as a two-way direct communication technology that does not depend on normal neural signal output pathways, can realize direct communication between brain instructions and the external environment, and provides a new method for human interaction perception and regulatory feedback. Among them, the steady-state visual evoked potential has the advantages of fewer recording electrodes, strong anti-interference ability, and high information transmission rate, and is one of the most widely used technologies in the field of brain-computer interface. [0003] At present, the methods for identifying steady-state visual evoked potentials are usually base...

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Problems solved by technology

[0003] At present, the methods for identifying steady-state visual evoked potentials are usually based on the principles of spatial filtering and template matching, such as CCA, CORRCA, and FBCCA, but they have the following disadvantages: (1) Due to the complexity of the presentation process of SSVEP and the physiological structure of the subjects Different subjects may experience random stimulus delays due to differences in state and state, which will affect the accurate construction of spatial filtering; therefore, the correlation between the original signal and the EEG template obtained by the CCA-based method will cause a large error;( 2) After the EEG signal is coupled with the multi-scale noise signal, it is very weak and retains strong nonlinearity and non-stationarity; therefore, using a linear method to extract SSVEP with obvious nonlinear and non-stationary features will suppress the noise while suppressing the noise. Attenuation or loss of useful signals will seriously affect the detection sensitivity and recognition accuracy; (3) Due to the nature of the FHN non-periodic excitation system, the SSVEP eigenfrequency detection based on the FHN neuron system method can only achieve better results when the data length is 2s. As a result, when the data length increases, the non-periodic and nonlinear characteristics of FHN stochastic resonance do not match the steady-state characteristics of SSVEP, resulting in a decline in the recognition accuracy; on the other hand, when the data length is less than 2s, due to the Insufficient information is included, and the recognition accuracy rate will also drop

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