Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal
Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal
The brain-computer interface(BCI)based on motor imagery electroencephalography(EEG)shows great potential in neurorehabilitation due to its non-invasive nature and ease of use.However,motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions,leading to low decoding recognition rates with traditional neural networks.To address this,this paper proposed a three-dimensional(3D)convolutional neural network(CNN)method that learns spatial-frequency feature maps,using Welch method to calculate the power spectrum of EEG frequency bands,converted time-series EEG into a brain topographical map with spatial-frequency information.A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features.Comparative experiments demonstrated that the average decoding recognition rate reached 86.89％,outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.
关键词
运动想象脑电/脑机接口系统/空-频特征图/特征选择/信号解码
Key words
Motor imagery electroencephalography/Brain-computer interface/Spatial-spectral feature picture/Feature selection/Signal decoding
维普
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