The application of Gam-EEGNet with global attention mechanism in SSVEP classification
Steady-state visual evoked potential(SSVEP)is an essential signal type in brain-computer interface(BCI)systems,widely utilized in BCI research due to its high stability and ease of operation.While previous studies have achieved significant progress in SSVEP signal classification,challenges such as low signal-to-noise ratio,non-stationarity,and individual variability still persist.To further enhance the accuracy and practicality of SSVEP classification,this paper proposes a novel neural network architecture—Gam-EEGNet—that combines a global attention mechanism with EEGNet.EEGNet,known for its compact,efficient,and adaptive structure,plays a critical role in SSVEP signal processing.By incorporating a global attention mechanism into EEGNet,Gam-EEGNet can more accurately extract and represent SSVEP signal features,effectively reducing individual variability and noise interference.Experiments were conducted using SSVEP EEG data encompassing 12 different frequencies,and the performance of Gam-EEGNet was compared with that of other mainstream deep learning methods,including CCNN,FB-tCNN,and SSVEPNet.The results demonstrate that Gam-EEGNet outperforms these methods in terms of classification accuracy and information transfer rate(ITR)across different time windows,particularly achieving a classification accuracy of 86.58%within a short 0.7 s time window.In a 1 s time window,the average recognition accuracy across multiple subjects exceeded 95%,with an ITR above 189 bits/min.Moreover,Gam-EEGNet showed better convergence and stability during training,with faster convergence and lower training errors.These results indicate that Gam-EEGNet offers significant performance improvements in SSVEP signal classification,making it especially suitable for real-time BCI systems requiring rapid response,with broad application potential.
deep learningbrain-computer interfacesteady-state visual evoked potentialsglobal attention mechanism
万方数据
维普
