Wimi Developed A New Deep Learning Method For Bmi Signals With Data Enhancement

WiMi Hologram Cloud Inc. has developed a new deep learning method that combines data augmentation and empirical modal decomposition techniques for classifying motor imagery signals. The method applies empirical modal decomposition to EEG frames, mixes their intrinsic modal functions to create new artificial EEG frames, and converts all EEG data into tensors as inputs to a neural network. Two neural networks combining CNN and wavelet neural networks are proposed to train the weights and classify the two types of motion picture signals. This novel deep learning method has the potential to improve the accuracy and generalization ability of the classifier.