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Improved EEG Decoding Method Based on Spiking Neural Network for SSVEPs’ Classification

文献类型： 会议论文

第一作者： Yufan He

作者： Yufan He 1 ; Yong Huang 2 ; Yan Li 1 ; Haoming Chu 2 ; Lirong Zheng 2 ; Ning Ma 2 ; Yuxiang Huan 2 ;

作者机构： 1.State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory for Biomaterials and Devices School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

2.Brain-Inspired Computing Laboratory, Guangdong Institute of Intelligence Science and Technology, Zhuhai, China

关键词： Training;Electrodes;Neuromorphic engineering;Computational modeling;Brain modeling;Electroencephalography;Encoding

会议名称： International Conference on Neuromorphic Computing

主办单位：

页码： 291-296

摘要： This paper presents an electroencephalogram (EEG) signals decoding method based on an event-driven spiking neural network (SNN) model design for steady-state visual evoked potentials (SSVEP) tasks’ classification and recognition. Adopting the approach of data-driven ensemble empirical mode decomposition (EEMD) of little additional input parameters, the original signals is enhanced to obtain relatively accurate EEG data with valid SSVEP characteristics, which strengthen the signal-to-noise (SNR) at the stimulation frequency. A canonical correlation analysis (CCA) method is considered to be applied to extract frequency characteristics from the multi-lead EEG data, declining the amount of data and training overhead. The training samples with correlation coefficient vectors are sampled into sparse spike sequences through the level crossing (LC) coding and input directly to a resembled excitation–inhibition (E–I) balanced SNN model in the way of event-driven processing. With the sparse data representation, the SNN model adopts the approaches of sparse training and neuromorphic computing to perform pattern recognition for the SNN-compatible spikes. On the Wearable SSVEP-BCI dataset and BETA database provided by Tsinghua University for 12-target and 40-target EEG classification, this SNN method can achieve 96.95% and 89.72% classification accuracy, with 99.66 bits/min and 130.26 bits/min information transfer rate (ITR), respectively.

分类号： tp183-53

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