M. Raheel Bhutta

Note: Three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water

Given that approximately 80% of blood is water, we develop a wireless functional near-infrared spectroscopy system that detects not only the concentration changes of oxy- and deoxy-hemoglobin (HbO and HbR) during mental activity but also that of water (H2O). Additionally, it implements a water-absorption correction algorithm that improves the HbO and HbR signal strengths during an arithmetic task. The system comprises a microcontroller, an optical probe, tri-wavelength light emitting diodes, photodiodes, a WiFi communication module, and a battery. System functionality was tested by means of arithmetic-task experiments performed by healthy male subjects.

Single-trial lie detection using a combined fNIRS-polygraph system.

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.

A hybrid EEG-fNIRS BCI: Motor imagery for EEG and mental arithmetic for fNIRS

In this paper, we have combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNRIS) to make a hybrid EEG-NIRS based system for brain-computer interface (BCI). The EEG electrodes were placed on the motor cortex region and the NIRS optodes were set on the prefrontal region. The data of four subjects was acquired using mental arithmetic tasks and motor imageries of the left- and right-hand. The EEG data were band-pass filtered to obtain the activity (8~18 Hz). The modified Beer-Lambert law (MBLL) was used to convert the fNIRS data into oxy- and deoxy-hemoglobin (HbO and HbR), respectively. A common threshold between the two modalities was established to define a common resting state. The support vector machines (SVM) was used for data classification. Three control commands were generated using the prefrontal and motor cortex data. The results show that EEG and fNIRS can be combined for better brain signal acquisition and classification for BCI.

Classification of somatosensory cortex activities using fNIRS

Abstract The ability of the somatosensory cortex in differentiating various tactile sensations is very important for a person to perceive the surrounding environment. In this study, we utilize a lab‐made multi‐channel functional near‐infrared spectroscopy (fNIRS) to discriminate the hemodynamic responses (HRs) of four different tactile stimulations (handshake, ball grasp, poking, and cold temperature) applied to the right hand of eight healthy male subjects. The activated brain areas per stimulation are identified with the t‐values between the measured data and the desired hemodynamic response function. Linear discriminant analysis is utilized to classify the acquired data into four classes based on three features (mean, peak value, and skewness) of the associated oxy‐hemoglobin (HbO) signals. The HRs evoked by the handshake and poking stimulations showed higher peak values in HbO than the ball grasp and cold temperature stimulations. For comparison purposes, additional two‐class classifications of poking vs. temperature and handshake vs. ball grasp were performed. The attained classification accuracies were higher than the corresponding chance levels. Our results indicate that fNIRS can be used as an objective measure discriminating different tactile stimulations from the somatosensory cortex of human brain.

Hybrid EEG-NIRS based BCI for quadcopter control

In this paper, we have proposed a novel control strategy for a quadcopter control using brain signals. A brain-computer interface (BCI) technology is developed using hybrid electroencephalography - near-infrared spectroscopy (EEG-NIRS) system and two commands are used to operate the quadcopter. An active brain signal upon the users own will is generated using a motor imagery task and a reactive brain signal is generated by visual flickering of light. The reactive command is used for the triggering control of the quadcopter and the active command is used to navigate the quadcopter in the forward direction. Linear discriminant analysis is used to classify the brain activity in offline environment. The results indicate that the proposed scheme is suitable for the BCI control applications.

Hemodynamic signals based lie detection using a new wireless NIRS system

In this paper, we have demonstrated the ability of a new multi-channel near-infrared spectroscopy (NIRS) system to detect deception in a concealed information test (CIT) paradigm. Brain signals from prefrontal cortex area of four healthy male subjects are collected using two different machines for comparison. Oxy and deoxy-hemoglobin (HbO and HbR) signals are used to define the features and then data is classified using linear discriminant analysis (LDA). Results show that the difference in HbO and HbR signals during lie and truth can be detected by the new wireless NIRS system.

Effect of anodal tDCS on human prefrontal cortex observed by fNIRS

Transcranial direct current stimulation (tDCS) is one of the noninvasive brain stimulation methods that have been used to study many neuropsychiatric and neurological disorders in humans. tDCS can excite or inhibit the neurons depending upon its polarity. In this study, we have investigated the effect of anodal tDCS on human prefrontal cortex using functional near-infrared spectroscopy (fNIRS), which is a noninvasive neuroimaging technique. We have developed a new wireless fNIRS system compatible with EEG, and also developed a pad-type tDCS with variable current limits. Our wireless fNIRS system is composed of a microcontroller, an optical probe, tri-wavelength light emitting diodes (LEDs), photodiodes, WiFi communication module and battery. The developed tDCS system can generate the current in the range of 0.8 ~ 2.2 mA. To test the functionality of the systems, fNIRS data was recorded before and after the tDCS stimulation. The results of this study show that the anodal tDCS excites the neurons in the region of interest and this excitability is monitored using the fNIRS system.

Drowsiness detection in dorsolateral-prefrontal cortex using fNIRS for a passive-BCI

In this paper, we have investigated the feasibility of detecting drowsiness using hemodynamic brain signals for a passive brain-computer interface (BCI). Functional near-infrared spectroscopy (fNIRS) is used to measure the right dorsolateral-prefrontal brain region in order to investigate the hemodynamic changes corresponding to drowsy and alert states. The data is recorded using five drowsy subjects during a simulated car driving task. The recoded data are converted into oxy- and deoxy-hemoglobin (HBO and HbR) using the modified Beer-Lambert law (MBLL) for feature extraction and classification. Signal mean and signal slope are extracted using the spatio-temporal time windows as features. Linear discriminant analysis (LDA) and support vector machines (SVM) are used for the training and testing of the brain data. The classification accuracy obtained using offline analyses is 74% and 77% respectively. The results show that drowsy and alert states are distinguishable from the right dorsolateral prefrontal brain region. Also, fNIRS modality can be used for drowsiness detection for a passive BCI.

Drowsiness detection using fNIRS in different time windows for a passive BCI

In this research, we have investigated the detection of drowsiness activity in dorsolateral-prefrontal cortex in three different time windows (0~3 sec, 0~4 sec and 0~5 sec) using functional near-infrared spectroscopy (fNIRS). Five drowsy subjects participated in a simulated driving task while their brain activity is monitored using fNIRS. The recorded brain activity is segmented into three windows for the acquisition of signal mean, signal slope and number of peaks as features. The data in each window is classified using linear discriminant analysis to find best window size. The results show that the best accuracy is obtained using 0~5 sec window after classification. Although the classification accuracy in 0~4 sec window is lower than in 0~5 sec window, both accuracies are suitable for brain-computer interface applications (i.e. accuracy>70%). The accuracy in 0~3 sec window is less than 70% for two subjects. For driver drowsiness detection, high accuracy with quick detection time is required, therefore we propose drowsiness detection in 0~4 sec window using fNIRS monitoring.

Motor imagery performance evaluation using hybrid EEG-NIRS for BCI

In this paper, we have evaluated the performance of motor imagery (MI), before and after training by a rehabilitation robot, for brain-computer interface (BCI). A hybrid electroencephalography and near-infrared spectroscopy (EEG-NIRS) system is used to detect the MI by placing the electrodes and optodes around the motor cortex region. Five healthy subjects have participated in the experiment. The subjects are assisted by a rehabilitation robot in an arm movement paradigm during the training session. The MI activity of the subjects is recorded before and after the training sessions. The brain signals from the motor cortex are recorded simultaneously using EEG-NIRS. We found a significant improvement in the MI performance after training. Linear discriminant analysis is used to classify the acquired activity in an offline analysis. The data analysis shows that the hybrid EEG-NIRS can detect better motor activity than individual modality. The average classification accuracy of the subjects has increased from 66% to 94% after training. We propose that the training of the motor cortex by a rehabilitation robot can improve the MI performance for BCI.