SSVEP classification method based on one-dimensional neural network and fast Fourier transform
The steady-state visual evoked potential(SSVEP)is a type of brain signal with distinct characteristics,induced by exogenous visual stimuli,and has advantages such as quick training and low data requirements.However,the recognition and classification of SSVEP remains a challenge.This paper discusses an SSVEP signal classification method based on a one-dimensional convolutional neural network and fast fourier transform(FFT-1DCNN)to meet the needs of non-invasive brain-computer interface(BCI)systems for cross-user classification.The paper utilizes a one-dimensional convolutional neural network to capture local dependencies and patterns,and employs fast Fourier transform to convert multi-channel one-dimensional time-domain signals into two-dimensional spectra.This study uses a cost-effective 8-channel EEG signal amplifier and a specially made EEG headband to collect EEG data from six subjects,ensuring data quality through data preprocessing,and validates the effectiveness of the proposed algorithm based on a proprietary dataset in both user-dependent and user-independent training scenarios.In scenarios where users are dependent,this method surpasses the accuracy of the TRCA algorithm by 3.14％.In user-independent contexts,it outperforms the CCA,FBCCA,and C-CNN algorithms by 10.29％,4.72％,and 2.26％,respectively,proving its superior effectiveness.
万方数据
维普
