Melissa Jiyoun Hong

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.

The hybrid brain-computer interface (BCI)s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

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.

Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.

Classification performance analysis of combined fNIRS-polygraph system using different temporal windows

Polygraph has been widely used to detect lie which measures some physiological parameters in human body but many researchers believe that lie can be detected more precisely if we can find the neuronal changes which occur during a deception process. To investigate the hemodynamic response related to deception, functional near-infrared spectroscopy (fNIRS) is used at the prefrontal cortex (PFC) of the subject. Commercially available polygraph system, Paragon acquisition system (PAS) is used to measure the physiological parameters like respiration and electrodermal activity (EDA) during the deception process. A mock crime scenario with a stimulus of single trial is used as a deception protocol. The acquired data is classified into true and lie classes, based on hemoglobin concentration changes in case of fNIRS and based on the physiological signal changes for polygraph data. Linear discriminant analysis (LDA) is used as a classifier. In this paper, we have analyzed the effect of different temporal windows in classification of combined fNIRS-polygraph signals for lie detection. The results show that the best time window for fNIRS-polygraph system is 2-7 s and 0-7 s for fNIRS and polygraph data respectively.

Non-lateralization with noise in the auditory cortex: An fNIRS study

Functional near-infrared spectroscopy (fNIRS) technique is used to measure concentration of hemodynamic changes (i.e., oxy- and deoxy-hemoglobin) in the human brain, while a subject is performing specific cognitive tasks. The advance brain functions related to music are thought to implicate uniquely human abilities, and it is known to have a strong tendency for hemispheric lateralization in the brain. The purpose of this paper is to investigate the effects of lateralization brain function in auditory cortex using fNIRS for music stimuli with background noise. It was found that the response to music with different background noise will affect different lateralization as indicated in the activation maps.