Zafar Amad, Hong Keum-Shik
School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, South Korea.
School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, South Korea; Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, South Korea.
Biomed Opt Express. 2016 Dec 19;8(1):367-383. doi: 10.1364/BOE.8.000367. eCollection 2017 Jan 1.
In this paper, the use of initial dips using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. Features and window sizes for detecting initial dips are also discussed. Three mental tasks including mental arithmetic, mental counting, and puzzle solving are performed in obtaining fNIRS signals from the prefrontal cortex. Vector-based phase analysis method combined with a threshold circle, as a decision criterion, are used to detect the initial dips. Eight healthy subjects participate in experiment. Linear discriminant analysis is used as a classifier. To classify initial dips, five features (signal mean, peak value, signal slope, skewness, and kurtosis) of oxy-hemoglobin (HbO) and four different window sizes (01, 01.5, 02, and 02.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 02.5 sec provide the best average classification accuracy of 57.5% for three classes. To further validate the result, three-class classification using the conventional hemodynamic response (HR) is also performed, in which two features (signal mean and signal slope) and 27 sec window size have yielded the average classification accuracy of 65.9%. This reveals that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks. Further improvement can be made by using deoxy hemoglobin signals in coping with the slow HR problem.
本文研究了利用功能近红外光谱技术（fNIRS）中的初始下降信号用于脑机接口（BCI）。还讨论了检测初始下降信号的特征和窗口大小。在从前额叶皮层获取fNIRS信号的过程中，执行了包括心算、心数和解谜在内的三项心理任务。基于向量的相位分析方法结合阈值圆作为决策标准，用于检测初始下降信号。八名健康受试者参与了实验。线性判别分析用作分类器。为了对初始下降信号进行分类，研究了氧合血红蛋白（HbO）的五个特征（信号均值、峰值、信号斜率、偏度和峰度）以及四个不同的窗口大小（01、01.5、02和02.5秒）。结果表明，信号均值和峰值的组合以及02.5秒的时间段对三类信号提供了最佳平均分类准确率，为57.5%。为了进一步验证结果，还使用传统的血液动力学响应（HR）进行了三类分类，其中两个特征（信号均值和信号斜率）以及27秒的窗口大小产生了65.9%的平均分类准确率。这表明，基于fNIRS的BCI使用初始下降信号检测可以将命令生成时间从7秒减少到2.5秒，而对于三项心理任务，分类准确率从65.9%略有下降到57.5%。通过使用脱氧血红蛋白信号来应对缓慢的HR问题，可以进一步改进。
