Method for Recognizing Motor Imagery Electroencephalography (MI-EEG) Signal Based on Capsule Network (CAPSNET)

A method for recognizing a motor imagery electroencephalography (MI-EEG) signal based on a capsule network (CapsNet), comprising the following steps:

• S1:
• mapping an electroencephalography (EEG) time series of an MI-EEG signal into a three-dimensional (3D) array form based on a spatial electrode distribution;
• S2:
• constructing, by using a CapsNet and 3D convolution, a three-dimensional capsule network (3D-CapsNet) model for recognizing the MI-EEG signal, and using an EEG signal in the 3D array form described in the S1 as an input of the 3D-CapsNet for recognizing the MI-EEG signal, wherein a 3D-CapsNet comprises a 3D convolution module and a CapsNet module;
• the 3D convolution module performs feature extraction on the input EEG signal in the 3D army form from both a temporal dimension and an inter-channel spatial dimension through a plurality of layers of 3D convolution to obtain a low-level feature;
• and the CapsNet module has a spatial detection capability, and the low-level feature output by the 3D convolution module is integrated through the CapsNet to obtain a high-level spatial vector containing an inter-feature relationship;
• and
• S3:
• training the CapsNet module by using a dynamic routing algorithm, connecting a primary capsule and a motor capsule through dynamic routing, and finally outputting a classification result through a nonlinear activation function squash.