使用fNIRS进行会话和受试者间鲁棒工作负荷水平对齐的域适应
Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS
AI总结：
针对fNIRS数据跨会话和受试者的域偏移问题，提出使用G-W和FG-W两种域适应方法进行工作负荷水平对齐，实验证明其性能显著优于SVM、CNN和RNN等监督方法。
摘要：
意义：我们展示了在功能近红外光谱（fNIRS）数据上使用域适应来分类涉及工作记忆的不同水平n-back任务的潜力。目标：fNIRS数据中的域偏移是不同实验会话和受试者间工作负荷水平对齐的一个挑战。为了解决这个问题，使用了两种域适应方法——Gromov-Wasserstein（G-W）和Fused Gromov-Wasserstein（FG-W）。方法：具体而言，我们使用来自一个会话或一个受试者的标记数据来分类另一会话（同一受试者内）或另一受试者中的试验。我们将G-W用于逐会话对齐，将FG-W用于逐受试者对齐，应用于在不同n-back任务水平期间采集的fNIRS数据。我们将这些方法与三种监督方法进行了比较：多类支持向量机（SVM）、卷积神经网络（CNN）和循环神经网络（RNN）。结果：在六名受试者的样本中，G-W在逐会话对齐中实现了68 $\pm$ 4 %的对齐精度（加权平均值$\pm$标准误差），FG-W在逐受试者对齐中实现了55 $\pm$ 2 %的对齐精度。在每种情况下，25 %的精度代表随机水平。G-W和FG-W的对齐精度结果均显著高于SVM、CNN和RNN。我们还表明，从fNIRS数据中去除运动伪影在提高对齐性能方面起着重要作用。结论：域适应具有通过使用fNIRS数据进行逐会话和逐受试者心理工作负荷对齐的潜力。
英文摘要：
Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory. Aim: Domain shift in fNIRS data is a challenge in the workload level alignment across different experiment sessions and subjects. In order to address this problem, two domain adaptation approaches -- Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) were used. Approach: Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels. We compared these approaches with three supervised methods: multi-class Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). Results: In a sample of six subjects, G-W resulted in an alignment accuracy of 68 $\pm$ 4 % (weighted mean $\pm$ standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 $\pm$ 2 % for subject-by-subject alignment. In each of these cases, 25 % accuracy represents chance. Alignment accuracy results from both G-W and FG-W are significantly greater than those from SVM, CNN and RNN. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. Conclusions: Domain adaptation has potential for session-by-session and subject-by-subject alignment of mental workload by using fNIRS data.
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