Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface

BioData Mining

Methodology

Haiqin Liu¹  Xiaoyong Ren¹  Jundong Li²  Qi Mao²  Xinhong Hei²  Zhenghao Shi²  Jing Luo² 

[1] Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China;Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China;Human-Machine Integration Intelligent Robot Shaanxi University Engineering Research Center, Xi’an University of Technology, Xi’an, Shaanxi, People’s Republic of China;

关键词: Brain-computer interface (BCI);    Multisubject BCI;    Motor imagery (MI);    Convolutional neural network (CNN);    Overlapping filter bank;   

DOI  :  10.1186/s13040-023-00336-y

 received in 2022-11-30, accepted in 2023-07-03,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMotor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.MethodsThis paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.ResultsExperiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.ConclusionThe proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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