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首页> 外文期刊>Journal of near infrared spectroscopy >Investigating the need for modelling temporal dependencies in a brain-computer interface with real-time feedback based on near infrared spectra
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Investigating the need for modelling temporal dependencies in a brain-computer interface with real-time feedback based on near infrared spectra

机译：研究基于近红外光谱在具有实时反馈的脑机接口中对时间依赖性建模的需求

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Near infrared (NIR) spectroscopy is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in haemoglobin concentrations in neurocortical tissue. Previous NIR spectroscopy studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. In particular, it is unclear whether or not the temporal dependencies of haemodynamic changes ought to be considered in online classification. To answer this question, this study contrasted online classification of prefrontal haemodynamics using NIR spectra processed using two approaches: an artificial neural network (ANN) that considered instantaneous samples of oxy- and deoxy-haemoglobin concentrations (i.e. ignored temporal dependencies) and a hidden Markov model-based (HMM) classifier which modelled a temporal sequence of concentrations (i.e. embodied temporal dependencies). Both classifiers were implemented for online operation with immediate visual feedback via a monitor showing a vertical bar the height of which was contingent on the classifier's output. Ten subjects participated in two study sessions each, one with each type of classifier. Participants were cued to raise and lower the bar in alternating 20 s intervals using mental fast singing and focused breathing, respectively. Only the ANN classifier facilitated online classification rates greater than chance (P=0.0289). The influence of physiological noise on online classification of prefrontal haemodynamics was deemed to be minimal via offline analysis of concurrently measured respiration and blood pulse. Nine of the ten participants reported using the feedback to alter their activation strategy. Mental fatigue, task repetitiveness and the lack of ambient lighting were identified as factors compromising performance in half the participants. The inferior performance of the HMM classifiers suggests that modelling of the temporal dynamics of haemoglobin concentration changes may not be necessary in an online NIR-BCI. Further study of online NIR-BCIs with instantaneous feedback is warranted.
机译：近红外（NIR）光谱学是一种新兴的非侵入性脑机接口（BCI）模式，用于测量神经皮质组织中血红蛋白浓度的变化。以前的NIR光谱学研究并未采用在线分类的实时反馈，这种结合可以使用户即时更改其心理策略。特别是，尚不清楚在线分类中是否应考虑血液动力学变化的时间依赖性。为了回答这个问题，本研究对比了使用两种方法处理的近红外光谱对前额叶血流动力学的在线分类：一种人工神经网络（ANN），它考虑了氧和脱氧血红蛋白浓度的瞬时样本（即忽略了时间依赖性）和隐马尔可夫基于模型的（HMM）分类器，对浓度的时间序列（即体现的时间依赖性）进行建模。这两个分类器均实现了在线操作，并通过监视器显示了即时的视觉反馈，该监视器显示了一个垂直条，其高度取决于分类器的输出。十名受试者分别参加了两次学习课程，每种学习者都使用了一种分类器。提示参与者分别使用精神快速歌唱和集中呼吸在20秒间隔内升高和降低杠铃。只有ANN分类器促进在线分类率大于机会（P = 0.0289）。通过同时测量呼吸和脉搏的离线分析，生理噪声对前额血流动力学在线分类的影响被认为是最小的。十个参与者中有九个报告使用反馈来更改其激活策略。精神疲劳，任务重复和缺乏环境照明被认为是损害一半参与者表现的因素。 HMM分类器的劣等性能表明，在在线NIR-BCI中可能不需要对血红蛋白浓度变化的时间动态建模。需要对具有瞬时反馈的在线NIR-BCI进行进一步研究。

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