Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.

中文翻译：

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矢量相位分析方法通过检测基线状态来优化心理工作量估算

先前的任务诱发的血流动力学响应（HR）的基线返回不正确可能会导致随后的HR发生较大变化，从而影响脑机接口系统中精神工作量的估计。在这项研究中，我们提出了一种使用矢量相位分析的方法来检测基线状态为最佳或次优。我们假设选择最佳基准块中观察到的与神经元相关的HR可以导致估计精神负荷的改善。氧合和脱氧血红蛋白浓度变化被整合为载体相的一部分。拟议的方法应用于块设计的功能近红外光谱数据集（总块= 1384），对执行多个难度级别的心理算术任务的24名受试者进行了测量。使用该方法检测到的最佳基线和次最佳基线之间的血流动力学信号变化存在显着差异。这支持了所提出的方法在检测基线状态以更好地估计心理负荷方面的有效性。结果进一步强调了定制恢复持续时间的必要性。简而言之，所提出的方法提供了一种实用的方法来检测任务诱发的信号，而无需额外的探针。