Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs

Jing Luo; Weiwei Shi; Na Lu; Jie Wang; Hao Chen; Yaojie Wang; Xiaofeng Lu; Xiaofan Wang; Xinhong Hei

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Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs

机译：使用双级联Softmax CNNS提高基于多功能电动机图像的BCIS的性能

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Objective. Motor imagery (MI) EEG signals vary greatly among subjects, so scholarly research on motor imagery-based brain–computer interfaces (BCIs) has mainly focused on single-subject systems or subject-dependent systems. However, the single-subject model is applicable only to the target subject, and the small sample number greatly limits the performance of the model. This paper aims to study a convolutional neural network to achieve an adaptable MI-BCI that is applicable to multiple subjects. Approach. In this paper, a twin cascaded softmax convolutional neural network (TCSCNN) is proposed for multisubject MI-BCIs. The proposed TCSCNN is independent and can be applied to any single-subject MI classification convolutional neural network (CNN) model. First, to reduce the influence of individual differences, subject recognition and MI recognition are accomplished simultaneously. A cascaded softmax structure consisting of two softmax layers, related to subject recognition and MI recognition, is subsequently applied. Second, to improve the MI classification precision, a twin network structure is proposed on the basis of ensemble learning. TCSCNN is built by combining a cascaded softmax structure and twin network structure. Main results. Experiments were conducted on three popular CNN models (EEGNet and Shallow ConvNet and Deep ConvNet from EEGDecoding) and three public datasets (BCI Competition Ⅳ datasets 2a and 2b and the high-gamma dataset) to verify the performance of the proposed TCSCNN. The results show that compared with the state-of-the-art CNN model, the proposed TCSCNN obviously improves the precision and convergence of multisubject MI recognition. Significance. This study provides a promising scheme for multisubject MI-BCI, reflecting the progress made in the development and application of MI-BCIs.

机译：客观的。电机图像（MI）EEG信号在受试者中有很大差异，因此学术研究基于电机图像的脑电脑接口（BCIS）主要集中在单亲系统或主题依赖系统上。但是，单个主题模型仅适用于目标主题，小样本数大大限制了模型的性能。本文旨在研究卷积神经网络，实现适用于多个受试者的适应性MI-BCI。方法。本文提出了一个双级联Softmax卷积神经网络（TCSCNN），用于多相属MI-BCIS。所提出的TCSCNN是独立的，可以应用于任何单对象MI分类卷积神经网络（CNN）模型。首先，为了减少个体差异的影响，同时实现主题识别和MI识别。随后施加由两个软MAX层组成的级联软MAX结构，随后施加有关识别和MI识别。其次，为了提高MI分类精度，基于集合学习提出双网络结构。 TCSCNN是通过组合级联的Softmax结构和双网络结构来构建的。主要结果。实验是在三个流行的CNN模型（EEGNET和EGDECODING）和三个公共数据集（BCI竞赛ⅳdatasets 2a和2b和高伽玛数据集）上进行的实验，以验证所提出的TCSCNN的性能。结果表明，与最先进的CNN模型相比，所提出的TCSCNN明显提高了多功能MI识别的精度和收敛性。意义。本研究提供了多功能MI-BCI的有希望的方案，反映了MI-BCIS的开发和应用所取得的进展。

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来源
《Journal of neural engineering》 |2021年第3期|036024.1-036024.13|共13页
作者
Jing Luo; Weiwei Shi; Na Lu; Jie Wang; Hao Chen; Yaojie Wang; Xiaofeng Lu; Xiaofan Wang; Xinhong Hei;
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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;

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;

State Key Laboratory for Manufacturing Systems Engineering Systems Engineering Institute Xi'an Jiaotong University Xi'an Shaanxi People's Republic of China;

State Key Laboratory for Manufacturing Systems Engineering Systems Engineering Institute 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;

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;

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;

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;

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;

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正文语种 eng
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关键词
brain-computer interface (BCI); multisubject BCI; motor imagery (MI); convolutional neural network (CNN);

机译：脑电脑界面（BCI）;MultiSubject BCI;电机图像（MI）;卷积神经网络（CNN）;

Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs

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