Investigation of initial dips in mental arithmetic tasks

Amad Zafar,Keum-Shik Hong,M. Jawad Khan 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10

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).

Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study

Zafar, Amad,Hong, Keum-Shik World Scientific 2018 International journal of neural systems Vol.28 No.10

Determination of the parameters in the designed hemodynamic response function using Nelder-Mead algorithm

Amad Zafar,Usman Ghafoor,M. Atif Yaqub,Keum-Shik Hong 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10

In this paper, we have investigated the use of the Nelder-Mead algorithm in determining the parameters of a designed hemodynamic response function (dHRF) instead of using fixed parameters for functional near-infrared spectroscopy (fNIRS). The hemodynamic response (HR) is supposed to be a linear combination of the baseline, the dHRF, and physiological noises (Mayer, cardiac, and respiration). The linear combination of three gamma functions is used to model the dHRF including the initial dip, the conventional HR, and the undershoot afterward. In this formulation, fifteen parameters (9 for dHRF and 6 for physiological noises) are unknown. An objective function is designed and solved using the iterative optimization Nelder-Mead algorithm to determine the unknown parameters of dHRF and physiological noises. The performance of the algorithm is tested using simulated and experimental datasets. The fNIRS experimental data were acquired from five healthy subjects during right-hand thumb finger flexion/extension tasks from the left motor cortex. The results demonstrate that inter-subject differences existed in the dHRF parameters. Therefore, it will be worthwhile to use subject-dependent dHRF parameters for a better estimation of the cortical activation using fNIRS.

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

Amad Zafar,M. Jawad Khan,Keum-Shik Hong 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

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.

A Novel Approach for Blind Estimation of Reverberation Time using Gamma Distribution Model

Amad Hamza,Tariqullah Jan,Asiya Jehangir,Waqar Shah,Haseeb Zafar,M. Asif 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

A Novel Approach for Blind Estimation of Reverberation Time using Gamma Distribution Model

Hamza, Amad,Jan, Tariqullah,Jehangir, Asiya,Shah, Waqar,Zafar, Haseeb,Asif, M. The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

Enhancement in classification accuracy of motor imagery signals with visual aid: An fNIRS-BCI Study

Usman Ghafoor,Amad Zafar,M. Atif Yaqub,Keum-Shik Hong 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10

One of the most promising brain activity utilized in brain-computer interface (BCI) is motor imagery (MI). Due to weak hemodynamic response (HR) signal, the achieved classification accuracies using MI are not sufficiently high. In this study, the enhancement in HR was investigated during motor imagery tasks of ball squeezing with the right hand. Brain signals in the form of concentration changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) from the left sensorimotor cortex were obtained using functional near-infrared spectroscopy (fNIRS). The experiment was separated in two sessions: In the first session the MI task was performed without a visual aid, and in the second session of the same task, the visual aid was provided: A video was played on a screen that showed a person continuously squeezing the ball, which can help in enhancing the imagination, thus improvement in HR. Later the features of averaged ΔHbO were used for classification. The active channels were selected on the basis of t-values and trials of those channels were mean to obtain averaged ΔHbO. Consistent with literature, imagery task with visual aid, showed increased activation in ΔHbO. Moreover, linear discriminant analysis was used to classify signals by taking the mean and peak of the averaged ΔHbO resulting in average classification accuracies of approximately 66% and 77% for MI task, with and without visual aid, respectively. These results are convincing that showed improvement in MI ability which will be useful for fNIRS-based BCI applications.

Cortical activation during voluntary and passive movement of human index finger

Usman Ghafoor,Amad Zafar,Keum-Shik Hong 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10

Passive movement, motor imagery, and observation of movement have been able to activate the somatosensory and motor cortex of brain without voluntary movement. In this paper, we have investigated cortical activation in brain using a finger movement passive mode together with voluntary movement and analysis is based on within-subject design protocol. Five subjects have participated in movement of finger tasks meanwhile their brain activity is monitored using functional near-infrared spectroscopy (fNIRS). The two gamma functions are used to model the conventional hemodynamic response function (cHRF) to determine the active locations. The t-maps (brain maps) are generated for the comparison of oxy-hemoglobin (HbO) responses of both voluntary and passive movement of index finger tasks. The results have demonstrated that both movement modes activated sensorimotor areas. The neural activation pattern in voluntary execution of finger is somewhat similar to passive movement. The results of this study will help in checking whether passive movements can be able to induce suitable amount of somatosensory stimulation. Our results inveterate that the voluntary and passive movement task are strongly coupled, supporting the importance of passive tasks as a diagnostic tool in the clinical setting.

Existence of Initial Dip for BCI: An Illusion or Reality

Hong, Keum-Shik,Zafar, Amad Frontiers Media S.A. 2018 Frontiers in neurorobotics Vol.12 No.-

<P>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.</P>

Detection and Classification of Three-Class Initial Dips Using Vector Phase Analysis with Dual Threshold Circles: An fNIRS Study

M. N. Afzal Khan,Amad Zafar,Keum-Shik Hong 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10

In this paper, we have used dual-threshold circles in the vector phase analysis to maximize classification accuracy of three class initial dip in functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI). fNIRS signals for three different mental tasks (mental arithmetic, mental counting and puzzle solving) are measured from prefrontal cortex of eight healthy subjects. After the acquisition of signals, filters are applied to remove different noises (cardiac, respiratory and Mayer). A dual threshold-based vector phase analysis is used for the detection of initial dip. Features used for classification of initial dips are signal mean, signal slope, skewness, kurtosis and peak in three different window sizes 0~1, 0~1.5 and 0~2 sec. The features are then passed from multi-class linear discriminant analysis for classification of three different initial dips. The average classification accuracy in offline analysis obtained using dual threshold circles in the vector phase analysis is 69.12 % in the case of window size 0~2 sec. These results are a significant improvement compared to the existing literature and are more reliable for use in BCI applications.