Xiao-Su Hu

Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

BackgroundNear-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data.MethodsIn this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t-statistical test. The t-statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework.ResultsThe obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated.ConclusionsThis paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.

fNIRS-based online deception decoding

Deception involves complex neural processes in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate deception and truth-telling. In this paper, a functional near-infrared spectroscopy (fNIRS)-based online brain deception decoding framework is developed. Deploying dual-wavelength fNIRS, we interrogate 16 locations in the forehead when eight able-bodied adults perform deception and truth-telling scenarios separately. By combining preprocessed oxy-hemoglobin and deoxy-hemoglobin signals, we develop subject-specific classifiers using the support vector machine. Deception and truth-telling states are classified correctly in seven out of eight subjects. A control experiment is also conducted to verify the deception-related hemodynamic response. The average classification accuracy is over 83.44% from these seven subjects. The obtained result suggests that the applicability of fNIRS as a brain imaging technique for online deception detection is very promising.

Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series.

Near-infrared spectroscopy (NIRS) can detect two different kinds of signals from the human brain: the hemodynamic response (slow) and the neuronal response (fast). This paper explores a nonlinear aspect in the tactile-stimulus-evoked neuronal optical response over a NIRS time series (light intensity variation). The existence of the fast optical responses (FORs) over the time series recorded in stimulus sessions is confirmed by event-related averaging. The chaos levels of the NIRS time series recorded both in stimulus and in rest sessions are then identified according to the estimated largest Lyapunov exponent. The obtained results ascertain that stimulus-evoked neuronal optical responses can be detected in the somatosensory cortex using continuous-wave NIRS equipment. Further, the results strongly suggest that the chaos level can be used to recognize the FORs in NIRS time series and, thereby, the state of the pertinent brain activity.

Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity

Abstract. The reduction of trial-to-trial variability (TTV) in task-evoked functional near-infrared spectroscopy signals by considering the correlated low-frequency spontaneous fluctuations that account for the resting-state functional connectivity in the brain is investigated. A resting-state session followed by a task-state session of a right hand finger-tapping task has been performed on five subjects. Significant ipsilateral and bilateral resting-state functional connectivity has been detected at the subjects’ motor cortex using the seed correlation method. The correlation coefficients obtained during the resting-state are used to reduce the TTV in the signals measured during the task sessions. The results suggest that correlated spontaneous low-frequency fluctuations contribute significantly to the TTV in the task evoked fNIRS signals.

Feature extraction based on common spatial analysis for time domain parameters

In this paper, an approach of feature extraction by designing common spatial filters specifically for time domain parameters (TDP) is proposed. This approach is aiming at motor imagery detection in electroencephalogram (EEG). Particularly, this method calculates the derivatives of the original signals and then applies common spatial analysis (CSP) to each order of derivatives. Variances of the spatially filtered signal after taking logarithm are used as features. Quadratic discriminant analysis (QDA) is applied to the feature vectors and classifies the vectors into different categories. We evaluate our approach using data consisting of two classes: left-hand and right-hand movement imageries from three subjects, and comparison between the proposed method and applying CSP analysis to the whole set of EEG signal directly is presented. Our results show that the proposed method generates more discriminant features in this motor imagery classification issue.

A functional near-infrared spectroscopy based lie detector

Deception involves complex neural processes and correlates in the brain. Different techniques have been used to study and understand brain mechanisms during deception. Moreover, efforts have been made to develop schemes that can detect and differentiate the deception and truth telling. In this paper, a functional near-infrared spectroscopy (fNIRS) based online brain deception decoding framework is developed. Deploying a dual-wavelength functional near-infrared spectroscopy, we interrogated sixteen sites around the forehead locations while eight able-bodied adults performed in separately deception scenarios and truth-telling scenarios. With the combination of preprocessed oxy-hemoglobin and deoxy-hemoglobin signals, we developed subject-specific classifiers using the support vector machine.

NIRS based brain activation mapping: A real-time application

Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been used to measure changes in cerebral blood oxygenation associated with brain activity. To date, there is no standard method for analyzing NIRS data, especially for real-time brain imaging applications. In this work, a novel real-time NIRS signal analysis framework based on the general linear model (GLM) and the Kalman estimator was devised. A set of simulated data was processed using the proposed framework. The results so obtained suggested that the method can effectively locate brain activation areas in real-time, thereby demonstrating its potential for real-time NIRS-based brain imaging applications.

Independent component analysis based trial-to-trial variations reduction in fNIRS signal

Functional near-infrared spectroscopy (fNIRS) is emerging optical brain imaging technique. It measures the hemodynamic changes that effectively reflect the brain states. However, the fNIRS signal analysis is sensitive to the trial-to-trial variability (TTV), and the source of the TTV remains elusive. Previous functional magnetic resonance image (fMRI) study suggested that the TTV can be attributed to spontaneous low frequency hemodynamic fluctuation in fMRI signal. Meanwhile, recent fNIRS studies confirm that the spontaneous fluctuation in fNIRS signal can be robustly detected by functional near infrared spectroscopy (fNIRS). In this paper, we investigate the relationship between TTV and the spontaneous fluctuations in fNIRS signal. The subject is asked to complete a right finger tapping experiment. Independent component analysis as well as functional connectivity is used to reduce the TTV in the finger tapping experiment. The result suggests that the low frequency spontaneous fluctuation contribute significantly to the TTV in fNIRS signal.

Extended general linear model based on-line brain activation mapping

Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been used to measure changes in cerebral blood oxygenation associated with brain activity. Numbers of research groups have applied general linear model (GLM) based method to analyze the NIRS data. However, classical GLM based method cannot provide on-line analysis. Therefore, its usage is constrained in processing NIRS data where real-time feedback is required. In the present paper, we are proposing a framework for NIRS based on-line brain activation mapping. The framework employs an extended GLM with coefficients updated by an extended Kalman particle filter for on-line brain activation mapping. A set of data recorded in a finger tapping experiment was studied using the proposed framework. The results so obtained, suggested that the method can effectively locate brain activation areas on-line with the noisy NIRS signal, thereby demonstrating its potential for real-time NIRS-based brain imaging applications.

Brain computer interface based on nonlinear characteristics identification of neuronal activities evoked optical properties

Brain computer interface (BCI) technology has been developed for decades as an alternate mode of communication for disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brain stem stroke and spinal cord injury. Near-infrared spectroscopy has recently been investigated as a non-invasive brain imaging method for developing BCI. Previous research has shown that task related hemodynamic signal recorded by NIRS from the cortex can be distinguished. However, the hemodynamic signal is a slow response for building a BCI. Near-infrared spectroscopy (NIRS) can detect two different kinds of signals from the human brain: the hemodynamic (slow) optical response, and the neuronal (fast) optical response. In this paper, we conducted a pilot study on investigating the feasibility of using fast optical response for building a NIRS-BCI. We explore the nonlinear aspects of the tactile-stimulus-evoked neuronal optical response (fast optical response) over a NIRS time series (light intensity variation). The fast optical responses (FORs) over time series recorded in stimulus sessions are confirmed by event-related averaging. The chaos levels of NIRS time series recorded both in stimulus and in rest sessions are then identified according to the estimated largest Lyapunov exponent. The obtained results strongly suggest that the chaos level can be used to recognize the FORs in NIRS time series and, thereby, the state of the pertinent brain activity.