Kwon Jinuk, Im Chang-Hwan
Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
Department of Electronic Engineering, Hanyang University, Seoul, South Korea.
Front Hum Neurosci. 2021 Mar 12;15:646915. doi: 10.3389/fnhum.2021.646915. eCollection 2021.
Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain-computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network (CNN)-based approach for implementing a subject-independent fNIRS-based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication (70%) as well as that obtained using conventional shrinkage linear discriminant analysis (65.74 ± 7.68%). To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials (of approximately 12 min) were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly.
功能近红外光谱技术（fNIRS）因其具有非侵入性、用户安全性高、价格低廉以及便于携带等优点，在脑机接口（BCI）领域受到了越来越多的关注。然而，fNIRS信号具有高度的个体特异性，且重测信度较低。因此，在每次使用基于fNIRS的BCI之前，都需要进行个体校准，以在实际的BCI应用中实现足够高的性能。在本研究中，我们提出了一种基于深度卷积神经网络（CNN）的新颖方法，用于实现独立于个体的基于fNIRS的BCI。共有18名参与者进行了基于fNIRS的BCI实验，实验的主要目标是区分心算任务和空闲状态任务。采用留一法交叉验证来评估所提出的独立于个体的基于fNIRS的BCI的平均分类准确率。结果表明，所提出方法的平均分类准确率为71.20±8.74%，高于有效BCI通信的阈值准确率（70%）以及使用传统收缩线性判别分析所获得的准确率（65.74±7.68%）。对于传统的依赖个体的基于fNIRS的BCI，要达到与所提出的独立于个体的基于fNIRS的BCI相当的分类准确率，需要24次训练试验（约12分钟）。预计我们基于CNN的方法将减少长期个体校准的必要性，从而显著提高基于fNIRS的BCI的实用性。
