A session-incremental learning system based on knowledge distillation and attention loss
Objective The brain-computer interface technology based on deep learning for motor imagery has a good development prospect in the field of intelligent rehabilitation.However,the motor imagery electroencephalogram(MI-EEG)signal is a non-stationary signal,its data distribution and feature space will change with the advancement of the rehabilitation process,which will cause the recognition ability of the convolutional neural network(CNN)model to decline.To enhance the temporal adaptability of the motor imagery(MI)decoding model,this paper proposes a session-incremental learning system(SILS)based on knowledge distillation and attention loss.Methods First,we performed band-pass filtering and down-sampling on the motor imagery EEG signals to enhance the information related to motor imagery.Next,a multi-branch,dual-attention,multi-module convolutional neural network was developed for extracting and integrating multi-scale temporal and spatial features from multi-lead MI-EEG data,utilizing an attention mechanism to amplify crucial channel and spatial information.Then,the ability of the incremental stage decoding model to continuously learn new knowledge and retain old knowledge was improved by using knowledge distillation technology and attention loss.Further,a small number of high-quality old samples were selected for data replay based on the nearest neighbor method to enhance the anti-forgetting performance of the incremental model.Finally,extensive experimental research was conducted by using the publicly available BCI Competition Ⅳ Dataset 2b,and the performance of SILS was verified through two indicators,plasticity and stability.Results SILS achieved average accuracies of 79.21％,79.05％,89.06％,88.38％,and 88.47％for stages 1 to 5,respectively,and the average forgetting rates of SILS for sessions 1 to 4 data in stage 5 were 9.72％,9.10％,9.88％,and 6.04％,respectively.Conclusions SILS has the ability to automatically adjust model parameters,maintain continuous learning and self-update,showing good temporal adaptability and performance stability.
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