Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning

• Main Authors: Wu, Xuanyu, Feng, Yixiong, Lou, Shanhe, Zheng, Hao, Hu, Bingtao, Hong, Zhaoxi, Tan, Jianrong
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; NeuCube; Spiking Neural Networks
• Online Access: https://hdl.handle.net/10356/172866
• Institution: Nanyang Technological University

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-parameters of the NeuCube reservoir have a great impact on its spatio-temporal representation, an improved cuckoo search algorithm is proposed to discover the optimal hyper-parameters for obtaining the optimal spatio-temporal features. Last but not least, a weighted transfer support vector machine is proposed to improve the original output classifier of the NeuCube in order to make the model adaptive to the cross-domain variability of EEG data. The proposed method is tested on open dataset 2a from BCI competition IV 2008 and achieves good spatio-temporal pattern recognition results. Furthermore, the neuron connectivity and activation level associated with the process of mental tasks are illustrated.