Wang Yuqing, Yang Zhiqiang, Ji Hongfei, Li Jie, Liu Lingyu, Zhuang Jie
Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Collage of Electronic and Information Engineering, Tongji University, Shanghai, China.
Department of Neurorehabilitation, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Front Psychol. 2022 Apr 7;13:833007. doi: 10.3389/fpsyg.2022.833007. eCollection 2022.
The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.
基于功能近红外光谱技术（fNIRS）的脑机接口（BCI）因其在情感识别方面巨大的应用潜力而受到越来越多的关注。然而，特征提取算法研究相对不足限制了其在实际中的应用。在本文中，为提高基于fNIRS的BCI的性能，我们提出了一种名为R-CSP-E的方法，该方法基于迁移学习和集成学习理论，在计算fNIRS信号特征时引入脑电信号（EEG）。具体来说，我们使用独立成分分析（ICA）算法来确定两种信号源之间的对应关系。然后，在基于改进的共同空间模式（CSP）算法计算空间滤波器时引入EEG信号。在公开数据集上的实验结果表明，本文提出的方法优于无迁移的传统方法。总体而言，平均分类准确率可提高多达5%。据我们所知，尝试在EEG和fNIRS之间应用迁移学习是一项创新。我们研究的结果不仅证明了迁移学习算法在跨模型脑机接口中的潜力，也为研究混合脑机接口提供了一个全新的视角。
