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.
Motor imagery electroencephalographyBrain-computer interfaceSpatial-spectral feature pictureFeature selectionSignal decoding
万方数据
维普
