Detection and classification of three-class initial dips from prefrontal cortex

Abstract

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 (0~1, 0~1.5, 0~2, and 0~2.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 0~2.5 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 2~7 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.

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

“…A number of previous studies reported that the peak of the initial dip occurred at approximately 1.9-2.5 s (Hu and Yacoub, 2012;Zafar and Hong, 2017). Therefore, q = 15 (i.e., 1.63 s since the sampling frequency was 9.19 Hz in this study) was selected for further analysis.…”

“…Early studies reported that the peak of initial dip occurred around 1.9-2.5 s (Malonek and Grinvald, 1996;Yacoub and Hu, 2001;Yacoub et al, 2001;Hu and Yacoub, 2012;Zafar and Hong, 2017). From this viewpoint, 1.63 s ahead prediction was selected in this study for an early detection of initial dips.…”

“…They showed that the initial dip could be detected in 0.9 s using the q-step-ahead prediction scheme, showing high potential for BCI applications. Later, Zafar and Hong (2017) attempted to find the features and temporal window size for classifying the initial dip duration in fNIRS signals of different mental tasks. They showed that the running temporal window size for fNIRS could be reduced from 5 to 2.5 s using initial dip features (i.e., signal mean and signal minimum) in the classification process.…”

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

“…* Corrected-p < 0.05, * * corrected-p < 0.01, and * * * corrected-p < 0.001 (false discovery rate correction). to improving the bitrate of fNIRS-BCIs (Cui et al, 2010;Zafar and Hong, 2017;Hong and Zafar, 2018), it is difficult to bridge the performance gap between fNIRS-BCIs and EEG-BCIs. However, for such SSVEP-BCI which is a type of exogenous BCIs, the need for an external stimulus causing user fatigue easily could be problematic.…”

Application of functional near-infrared spectroscopy in the healthcare industry: A review

“…4,9 Various algorithms and techniques have been developed and explored using fNIRS to improve the brain-computer interfaces (BCIs) to help physically disabled persons. [10][11][12][13][14][15] This paper reviews the research works conducted to advance the understanding of the e®ects of various diseases on our brain using fNIRS. These studies mostly involve patients with degenerative brain or psychiatric disorders.…”