Amad Zafar

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

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.

Initial dip detection based on both HbO and HbR vector-based phase analysis

A new threshold circle to minimize possible misclassification of initial dips in the functional near-infrared spectroscopy (fNIRS) signals using the vector-based phase analysis is investigated. In contrast to the work in [20] (i.e., the square root of the sum of the squares of oxy- and deoxy-hemoglobins), the peak value of oxy- or deoxy-hemoglobin during the resting state is used. The experiment was performed on five healthy subjects. The activity was measured using a frequency domain fNIRS system during the mental arithmetic task from the prefrontal cortex. With the new criterion, the radius of the circle becomes smaller than that in [20], and earlier detection of initial dips are possible. For the given arithmetic task, the channels of detecting the initial dips were not the same over the subjects, which reflects that the activated brain region in association with the performed task spreads in the prefrontal cortex. Also, the new method can further reduce the misinterpretation of large variations in the resting state and even during the task period.

Investigation of initial dips in mental arithmetic tasks: An fNIRS study

In this paper, we investigate the feasibility of identifying the functional near-infrared spectroscopy (fNIRS) signal occurred from a single trial arithmetic task, in which the rest state hemodynamic response (HR), the occurrence of an initial dip, and the regular hemodynamic response are involved. fNIRS signals are measured from five healthy subjects for mental arithmetic tasks from the prefrontal cortex. Multiclass linear discriminant analysis (LDA) is used in classifying the fNIRS signal upon a single trial. Four different features including the signal mean, skewness, signal slope, and kurtosis are compared with five different window sizes: 0∼1, 0∼1.5, 0∼2, 0∼2.5, and 0∼3 sec for classification. Threshold-based vector phase analysis method is used to ensure the presence of initial dips in fNIRS signals. The average classification accuracy in offline analysis of 65.3% in 0∼3 sec time window using signal mean and signal slope is obtained. The result shows that the initial dip can be classified from the baseline (rest) and HR by using signal mean and signal slope as a features. This will result in the reduction of time window size to 0∼3 sec in order to use fNIRS signals for brain-computer interface (BCI).

Existence of initial dip for BCI: An illusion or reality

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.

Comparison of brain areas for executed and imagined movements after motor training: An fNIRS study

In this paper, we investigate active brain regions for motor execution and motor imagination tasks after training with a rehabilitation robot. Functional near-infrared spectroscopy (fNIRS) is used to measure the hemodynamic responses in the motor cortices of five subjects. An assistive robot (IMT 2.0, connected to the right hand) is used during the training session to make the subject to reach a target point displayed on a computer screen. During the training, the subjects have to reach the target point in two directions (left and right) using right arm movement. Our intention is to investigate the differences between brain signals generated from left and right movements of the right hand. It was found that the same brain region was activated for both left- and right-directional motions. During the testing, we asked the subjects to imagine the executed movement. We found that although the imagined movement activity is weak but it appears in the same region as that of motor execution during the reach task. The results show that executed and imagined movements can be discriminated using fNIRS. However, for brain-computer interface it is difficult to generate two commands using only one arm movement signals.

Initial-dip based identification of the brain area for right-hand finger movement

In this paper, we have used a linear combination of three gamma functions to estimate the location of cortical activation during right-hand thumb finger (RHTF) flexion/extension using functional near-infrared spectroscopy (fNIRS). The three gamma functions are used to model the initial dip, conventional hemodynamic response, and undershoot of oxy-hemoglobin signals. The brain signals of five healthy subjects during RHTF flexion/extension task are acquired from the motor cortex. Vector phase analysis with a threshold circle as a decision criterion is used to ensure the presence of initial dips in fNIRS signals. The results show that the brain area around C3 during the RHTF flexion/extension task becomes highly activated for all subjects. Also, the t-map generated for the initial period, i.e., 2.5 sec, is more spatially localized than the t-map drawn for 15 sec period. The result demonstrated that the model obtained using the linear combination of three gamma functions can identify the initial dip brain region for motor activity task.

Hybrid EEG-NIRS based active command generation for quadcopter movement control

In this study, we have generated four active commands using hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for quadcopter control in online environment. Mental arithmetic, left hand clench imagery, and left and right eye-movements are used to navigate the quadcopter. Mental arithmetic task (decoded by fNIRS from the prefrontal cortex) is used to move the quadcopter in forward direction. The left hand clench imagery (decoded by EEG) is used to increase/decrease the height of flight. Signals generated using eye movement in the left/right direction is used for the rotation of the quadcopter. The flight is monitored by the user using the feedback from the frontal camera. The system is unaware of the obstacles in the environment. A fail-safe switch is incorporated in the program using steady-state evoked visual potentials (SSVEP) activity generated at 6 Hz. The fail-safe mechanism stops the forward movement of the quadcopter and the user can adjust the direction of the quadcopter to avoid any collisions with obstacles. The fail-safe trigger can be disabled by focusing on the 6 Hz signal trigger to resume the movement. The experiments conducted on 3 subjects show the possibility of controlling the quadcopter with active commands with increased safety features. The results show the feasibility of increasing commands using hybrid EEG-NIRS for brain-computer interface with applications to quadcopter control.