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.

Keywords: (070.5010) Pattern recognition; (170.2655) Functional monitoring and imaging; (200.3050) Information processing; (300.0300) Spectroscopy.

Fig. 1

BCI framework: Application of initial dip detection.

Fig. 2

Characteristics of the canonical initial dip.

Fig. 3

Experimental paradigms: Experiment 1 consists of mental arithmetic (MA), mental counting (MC), and puzzle solving (PS) tasks; Experiment 2 is composed of right-hand finger tapping (RHFT) and right-hand finger poking (RHFP); Experiment 3 examines checkerboard visualization; and a session consists of 5 trials.

Fig. 4

Electrode configuration in the prefrontal (Exp. 1), motor/somatosensory (Exp. 2), and visual cortices (Exp. 3).

Fig. 5

Vector plane with a threshold circle [–30].

Fig. 6

Phase portraits (from −5 to 15 sec) of mental arithmetic task (Sub. 1).

Fig. 7

The averaged HbOs and their standard deviations for MA, MC, and PS tasks.

Fig. 8

Receiver operating characteristic (ROC) curves for HR-based classification of 2~7 sec window using the averaged TPR and FPR for the MA, MC, and PS tasks over all subjects.

Fig. 9

ROC curves for the initial dip based classification in 0~2.5 sec window using the averaged TPR and FPR for the MA, MC, and PS tasks over all subjects.

Fig. 10

The averaged HbO and its standard deviation for MA, MC, PS, RHFT, RHFP, and checkerboard tasks.