Decoding working-memory load duringn-back task performance from high channel fNIRS data.

Christian Kothe, Grant Hanada, Sean Mullen, Tim Mullen
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Abstract

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

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从高通道 fNIRS 数据解码 n-Back 任务执行过程中的工作记忆负荷。
目的:功能性近红外光谱(fNIRS)可以通过大脑中的血氧变化测量神经活动,它采用了可穿戴的形式,能够在实验室内外的研究和实际职业环境中实现独特的应用。事实证明,fNIRS 能够测量认知状态,如精神工作量,通常使用基于机器学习(ML)的脑机接口(BCI)。迄今为止,这项研究主要依赖于通道数从十个以下到几百个不等的探头,不过最近出现了一类具有数千个通道的新型可穿戴近红外设备。这给 ML 分类带来了独特的挑战,因为 fNIRS 通常受限于较少的训练试验,从而导致严重的估计不足问题。到目前为止,人们还不太了解如何在实际 BCI 中最好地利用这种高分辨率数据,以及是否能实现最先进或更好的性能:为了解决这些问题,我们提出了一种 ML 策略来对工作记忆负荷进行分类,该策略依赖于时空正则化和其他科目的迁移学习,据我们所知,这种组合在以前的 fNIRS BCIs 中从未使用过。这种方法可被解释为端到端的广义线性模型,并允许使用通道级或皮层成像方法进行高度解释:我们的研究表明,使用所提出的方法,可以通过高分辨率 fNIRS 数据实现最先进的解码性能。我们还在由 43 名佩戴 3198 双通道 NIRS 设备的参与者组成的数据集上复制了几种最先进的方法,同时执行了 n-Back 任务:我们的方法有助于将高通道近红外设备确立为最先进的生物识别(BCI)的可行平台,并利用这类耳机开辟了新的应用领域,同时还实现了高分辨率模型成像和解释。
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