Browsing by Author "Chikara, Rupesh Kumar"

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Brain Activities Changes in Typical and Realistic Scenario-compatible Stop-signal Inhibitory Tasks and Classification Performance
(2013)
Ko, Li-Wei
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Chikara, Rupesh Kumar
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Liu, Yu-Ting
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生物科技學系
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Department of Biological Science and Technology
People regularly show inhibition control in split second under tough situations in daily life. In order to identify the brain process of inhibition control, this study introduced a new methodology to observe brain dynamics in response and success/failure inhibition in typical and realistic scenarios. Subjects will feel stress during decision-making in scenario-compatible stop-signal tasks in bith typical and realistic scenarios. The advantage of this research would be very useful in tough condition of daily life such as, driving and sport competition or in unusual situations like gun-fighting in the field of war and flooding. This study will develop two main issues: brain activity comparing between success and failure inhibition control and comparing brain activity during decision-making in normal scenario and in stress (realistic) scenario. The findings of the present study suggest that there is an increase in stressful condition during decision-making. However EEG results indicated that, in the parietal area, the event-related potential (ERP) appeared reverse phase relative to the frontal area. Therefore, we conclude that there is obvious difference in brain dynamics activities between success/failure inhibition in typical and realistic scenarios. In this research, we purposed Gaussian-classifier for typical and realistic scenarios. The classification average accuracy was observed 77.82% in typical and 75.67 % in realistic scenarios.
Classification of Brain Activities under Response Inhibition using Functional Connectivity
(2019-01-01)
Chikara, Rupesh Kumar
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Su, Kai-Hisang
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Ko, Li-Wei
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交大名義發表
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生物科技學系
Human response inhibition, the ability to suppress pre-potent behavior that is inappropriate or that is no longer required, is essential for behavior aimed at everyday life goals. In recent decades, researchers have shown a greater interest in inhibiting the response. Response inhibition is considered an operationalization of certain aspects of impulsivity and compulsivity. Impulsivity is commonly defined as a tendency to act by impulses, acts performed immediately and without voluntary control, while compulsivity is the tendency to repeat a specific behavior and to be unable to inhibit the behavior even when it is no longer appropriate. Due to the importance of inhibiting the response in everyday life, many neuropsychological models have been developed to test inhibitory performance. In this experiment, subjects were asked to respond to a visual stimulus (i.e., a square or circular figure), but withheld this response to irrelevant stimuli (i.e., alarm sound). Therefore, in this study, we developed a new classification model of brain activities under response inhibition using functional connectivity. In this proposed model, we classified the electroencephalography (EEG) signals of successful-stop and failed-stop trials. In this study, we used parametric (LDA, QDA) and non-parametric (PARZENDC, KNNC) classification algorithms to investigate the accuracy of this system. First, the EEG signals acquired during the successful-stop and failed-stop trials, after that, we measured the functional connectivity between seven brain regions, included F3-F4, F4-O1, F4-T8, T7-O1, T7-T8, C3-CZ and C4CZ by phase-locked value (PLV) method. These seven regions of interest covered the entire human brain. These functional connectivity values were used as an input feature for the proposed classification model. Our novel classification system achieved 88% accuracy with QDA. These new findings revealed that this model can be considered to classify the EEG signals of successful-stop and failed-stop trials during response inhibition.
Classification of EEG-P300 Signals Using Phase Locking Value and Pattern Recognition Classifiers
(2015)
Chikara, Rupesh Kumar
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Ko, Li-Wei
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生物科技學系
;
生物資訊及系統生物研究所
;
腦科學研究中心
In this paper, we present a classification method based on electroencephalogram (EEG) signal during left hand and right hand response inhibition (stop success vs stop fail) from different participants. The system uses phase locking value (PLV) for the features extraction and pattern recognition algorithm for classification. There are four classifiers: QDC, KNNC, PARZENDC and LDC used in this paper to estimate the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from C3-CZ and C4- CZ electrodes are calculated and then used as the feature and input for the classifiers algorithm. The classification system demonstrate an accuracy of 92 % in LDC. The results of this study suggest the method could be utilized effectively for response inhibition identification.
EEG Dynamics in Inhibition of Left-hand and Right-hand Responses during Auditory Stop-Signal Task
(2014)
Chikara, Rupesh Kumar
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Perumal, Ramesh
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Ko, Li-Wei
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Chen, Hsin
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生物科技學系
An experimental design is programmed using the presentation tool to investigate the global response inhibition process by quantifying the parameters such as inhibition efficiency, stop-signal delay (SSD) and stop-signal reaction time (SSRT) in the stop-signal paradigm. The aim of this study is to explore the response inhibition mechanisms in the left-hand and right-hand responses by using ERP and ERSP results obtained from the EEG data of different subjects. The inhibition efficiency of the right-hand response and left-hand response appears to be independent of each other as there is no significant difference between them. From these results, the inhibition mechanisms corresponding to these two regions of the brain may be viewed as statistically independent processes. Further, we inferred that the response inhibition mechanisms for both left-hand and right-hand responses have approximately the same spectral power observation analysis and we conclude that these processes are statistically independent of each other.
Exploration of Brain Connectivity during Human Inhibitory Control Using Inter-Trial Coherence
(2020-03-01)
Chikara, Rupesh Kumar
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Lo, Wei-Cheng
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Ko, Li-Wei
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交大名義發表
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生物科技學系
Inhibitory control is a cognitive process that inhibits a response. It is used in everyday activities, such as driving a motorcycle, driving a car and playing a game. The effect of this process can be compared to the red traffic light in the real world. In this study, we investigated brain connectivity under human inhibitory control using the phase lag index and inter-trial coherence (ITC). The human brain connectivity gives a more accurate representation of the functional neural network. Results of electroencephalography (EEG), the data sets were generated from twelve healthy subjects during left and right hand inhibitions using the auditory stop-signal task, showed that the inter-trial coherence in delta (1-4 Hz) and theta (4-7 Hz) band powers increased over the frontal and temporal lobe of the brain. These EEG delta and theta band activities neural markers have been related to human inhibition in the frontal lobe. In addition, inter-trial coherence in the delta-theta and alpha (8-12 Hz) band powers increased at the occipital lobe through visual stimulation. Moreover, the highest brain connectivity was observed under inhibitory control in the frontal lobe between F3-F4 channels compared to temporal and occipital lobes. The greater EEG coherence and phase lag index in the frontal lobe is associated with the human response inhibition. These findings revealed new insights to understand the neural network of brain connectivity and underlying mechanisms during human response inhibition.
Exploration of User's Mental State Changes during Performing Brain-Computer Interface
(2020-06-01)
Ko, Li-Wei
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Chikara, Rupesh Kumar
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Lee, Yi-Chieh
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Lin, Wen-Chieh
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交大名義發表
Substantial developments have been established in the past few years for enhancing the performance of brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user's mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user's visual area. BCI user's cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users' physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user's cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1-4 Hz), theta (4-7 Hz), and beta (13-30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1-4 Hz), alpha (8-12 Hz), and beta (13-30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user's cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.
Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study
(2019-09-01)
Chikara, Rupesh Kumar
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Ko, Li-Wei
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交大名義發表
;
生物科技學系
;
生物資訊及系統生物研究所
Auditory alarms are used to direct people's attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.
Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model
(2019-09-01)
Chikara, Rupesh Kumar
;
Ko, Li-Wei
;
交大名義發表
;
生物科技學系
;
生物資訊及系統生物研究所
Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.
Neural Mechanisms of Inhibitory Response in a Battlefield Scenario: A Simultaneous fMRI-EEG Study
(2016-05-02)
Ko, Li-Wei
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Shih, Yi-Cheng
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Chikara, Rupesh Kumar
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Chuang, Ya-Ting
;
Chang, Erik C.
The stop-signal paradigm has been widely adopted as a way to parametrically quantify the response inhibition process. To evaluate inhibitory function in realistic environmental settings, the current study compared stop-signal responses in two different scenarios: one uses simple visual symbols as go and stop signals, and the other translates the typical design into a battlefield scenario (BFS) where a sniper-scope view was the background, a terrorist image was the go signal, a hostage image was the stop signal, and the task instructions were to shoot at terrorists only when hostages were not present but to refrain from shooting if hostages appeared. The BFS created a threatening environment and allowed the evaluation of how participants' inhibitory control manifest in this realistic stop-signal task. In order to investigate the participants' brain activities with both high spatial and temporal resolution, simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings were acquired. The results demonstrated that both scenarios induced increased activity in the right inferior frontal gyrus (rIFG) and presupplementary motor area (preSMA), which have been linked to response inhibition. Notably, in right temporoparietal junction (rTPJ) we found both higher blood-oxygen-level dependent (BOLD) activation and synchronization of theta-alpha activities (4-12 Hz) in the BFS than in the traditional scenario after the stop signal. The higher activation of rTPJ in the BFS may be related to morality judgments or attentional reorienting. These results provided new insights into the complex brain networks involved in inhibitory control within naturalistic environments.
Neural oscillations in Temporoparietal lobes under Inhibitory Control in a Naturalistic situation
(2016)
Ko, Li-Wei
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Chikara, Rupesh Kumar
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Shih, Yi-Cheng
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Chang, Erik C.
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生物科技學系
The stop-signal paradigm has been commonly applied as a measure of inhibitory control due to its exclusive ability to capture theoretically important parameters related to the response inhibition. To observe the inhibitory function in realistic environmental settings, we designed a battlefield scenario (BFS), which is inspired by real condition like open fire between soldier and terrorist for saving civilian because terrorism is serious problem worldwide. The stop-signal task has been widely adopted as a way to parametrically quantify the response inhibition process. This study compared the response inhibition in two different scenarios: One used traditional simple visual symbols as go and stop signals i.e. symbol scenario (SBS), and the other translated the typical design into a battlefield scenario. The outcomes confirmed that both scenarios induced increased activity in the right inferior frontal gyrus (IFG) and pre supplementary motor area (preSMA), which have been associated to response inhibition. Particularly, in right temporoparietal junction (rTPJ) we found both higher BOLD activation and EEG power modulation in the battlefield scenario than in the traditional scenario after the stop stimulus onset.
Phase modulation-Based Response-Inhibition Outcome Prediction in Translational Scenario of Stop-Signal task
(2016-01-01)
Chikara, Rupesh Kumar
;
Ko, Li-Wei
;
生物科技學系
;
腦科學研究中心
;
Department of Biological Science and Technology
In this paper, a method is proposed to predict the resting-state outcomes of participants based on their electroencephalogram (EEG) signals recorded before the successful /unsuccessful response inhibition. The motivation of this study is to enhance the shooter performance for shooting the target, when their EEG patterns show that they are ready. This method can be used in brain-computer interface (BCI) system. In this study, multi-channel EEG from twenty participants are collected by the electrodes placed at different scalp locations in resting-state time. The EEG trials are used to predict two possible outcomes: successful or unsuccessful stop. Four classifiers (QDC, KNNC, PARZENDC, LDC) are used in this study to evaluation the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from 5-pair electrodes are calculated and then used as the feature input for the classifiers. Our experimental results show that the proposed method prediction accuracy (leave-one-out) was obtained 95% by QDC classifier.