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Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning by Xuanyu Wu; Yixiong Feng; Shanhe Lou; Hao Zheng; Bingtao Hu; Zhaoxi Hong; Jianrong Tan is a Computer Science article available to read on EtoBox.

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Electroencephalogram (EEG) data are produced in quantity for measuring brain activity in response to external stimuli. With the rapid development of brain-inspired intelligence, spiking neural network (SNN) possesses the potential to handle EEG data by using spiking activity transmitted among spatially located synapses and neurons. As an original and unifying SNN architecture, NeuCube, is developed to model, recognize and understand EEG data. However, the NeuCube still faces some challenges for EEG-based pattern recognition, such as few labeled data and changes of data probability distribution. Hence, this paper proposes a novel method to improve the performance of the NeuCube for EEG-based pattern recognition by transfer learning. In the first place, the covariance matrix alignment of EEG data is implemented for every subject in the Euclidean space, which reduces the probability distribution discrepancy of EEG data between different subjects. Different estimation methods for reference covariance matrix are tested and the optimal one is selected for different subjects. Secondly, spatio-temporal features of EEG data are extracted based on the NeuCube reservoir. Since hyper-parameter

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Author

Xuanyu Wu; Yixiong Feng; Shanhe Lou; Hao Zheng; Bingtao Hu; Zhaoxi Hong; Jianrong Tan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)