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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.
Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI
(2020-08-01)
Zhang, Hao-Yan
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Stevenson, Cory E.
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Jung, Tzyy-Ping
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Ko, Li-Wei
;
交大名義發表
Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming the impact of such user variability on BCI performance is an impending and inevitable challenge for routine applications of BCIs in the real world. To systematically explore the factors affecting BCI performance, this study embeds a Steady-State Visually Evoked Potential (SSVEP) based BCI into a "game with a purpose" (GWAP) to obtain data over significant lengths of time, under both high- and low-stress conditions. Ten healthy volunteers played a GWAP that resembles popular match-three games, such as Jewel Quest, Zoo Boom, or Candy Crush. We recorded the target search time, target search accuracy, and EEG signals during gameplay to investigate the impacts of stress on EEG signals and BCI performance. We used Canonical Correlation Analysis (CCA) to determine whether the subject had found and attended to the correct target. The experimental results show that SSVEP target-classification accuracy is reduced by stress. We also found a negative correlation between EEG spectra and the SNR of EEG in the frontal and occipital regions during gameplay, with a larger negative correlation for the high-stress conditions. Furthermore, CCA also showed that when the EEG alpha and theta power increased, the search accuracy decreased, and the spectral amplitude drop was more evident under the high-stress situation. These results provide new, valuable insights into research on how to improve the robustness of BCIs in real-world applications.
Intelligent agent for real-world applications on robotic edutainment and humanized co-learning
(2020-08-01)
Lee, Chang-Shing
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Wang, Mei-Hui
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Tsai, Yi-Lin
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Ko, Li-Wei
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Tsai, Bo-Yu
Dynamic assessment with an intelligent agent can differentiate the capabilities and proficiency of students. It can therefore be advocated as an interactive approach to conduct assessments on students in learning systems. Facebook AI Research proposed ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. They also developed Darkforest, which displays the competence and skills of high-level amateur Go players. To enable these open-source AI bots to assist humans at different levels in learning Go, this paper proposes an intelligent agent for real-world applications in robotic edutainment and humanized co-learning. To achieve this, we successfully constructed an OpenGo Darkforest (OGD) cloud platform using these AI bots and further combined the brain computer interface with the OGD cloud platform to observe the relationship between the brainwaves and win rates of human Go players. The intelligent agent also converted human brainwaves into physiological indices and reflected these in the robot to express human feelings or emotions in real-time. For future educational applications, this paper also presents intelligent robot teachers learning together with students in Taiwan and Japan. More than 200 students have been co-learning with intelligent robot teachers in Tainan, Kaohsiung, Taipei, and Tokyo from 2018 to 2019. The learning performance and feedback from students and teachers has been extremely positive, especially from remedial students.
Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task
(2020-06-01)
Ko, Li-Wei
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Komarov, Oleksii
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Lai, Wei-Kai
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Liang, Wei-Gang
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Jung, Tzyy-Ping
Objective. A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth (R) module. Main results. The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 +/- 0.019 s, and the r(2) is 0.885 +/- 0.057 according to a within-session leave-one-trial-out cross-validation. Significance. The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.
Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot
(2020-01-01)
Phang, Chun-Ren
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Ko, Li-Wei
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交大名義發表
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生物資訊及系統生物研究所
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National Chiao Tung University
Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals. Fourteen spatial connections (P3-Fp1, P3-F3, P3-F7, P3-C3, T5-F7, T5-C3, T5-T3, Fp2-T5, Fp2-P3, T6-Fp2, T6-T4, Cz-Fp1, Cz-F7 and Fp2-F7) found across twelve subjects significantly differed between the left and right foot MI, evidencing nonlocalized brain activity during MI. Foot MI were distinguished using machine learning algorithms in terms of the time- and frequency-domain connectivities extracted from Pearson & x2019;s correlation, multivariate autoregression (MVAR), bandpass correlation, and partial directed coherence (PDC) models. The results showed that connectivity extracted by pairwise Pearson & x2019;s correlation could be distinguished with 86.26 & x00B1; 9.95 & x0025;, while in the frequency-domain, the gamma band presented the best classification accuracy of 73.55 & x00B1; 17.11 & x0025;. We attempted to simulate asynchronous real-time classification paradigms in order to evaluate the classification performance of connectivity features compared to common spatial pattern (CSP) and band power (BP). The results indicate correlation-connectivity has the best outcome, attaining an accuracy of 80.75 & x00B1; 9.51 & x0025; in asynchronous classification.
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.
Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
(2020-04-01)
Komarov, Oleksii
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Ko, Li-Wei
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Jung, Tzyy-Ping
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交大名義發表
;
生物資訊及系統生物研究所
University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments. Often these factors result in long-term issues with daytime sleepiness, emotional instability, and mental exhaustion, which may lead to difficulties in the educational process. This study introduces the Daily Sampling System (DSS) implemented as a smartphone application, which combines a set of self-assessment scales for evaluating variations in the emotional state and sleep quality throughout a full academic term. In addition to submitting the daily sampling scores, the participants regularly filled in the Depression, Anxiety, and Stress Scales (DASS) reports and took part in resting-state EEG data recording immediately after report completion. In total, this study collected 1835 daily samples and 94 combined DASS with EEG datasets from 18 university students (aged 23-27 years), with 79.3 +/- 15.3% response ratio in submitting the daily reports during an academic semester. The results of pairwise testing and multiple regression analysis demonstrate that the daily level of self-perceived fatigue correlates positively with stress, daytime sleepiness, and negatively with alertness on awakening, self-evaluated sleep quality, and sleep duration. The spectral analysis of the EEG data reveals a significant increase in the resting-state spectral power density across the theta and low-alpha frequency bands associated with increased levels of anxiety and stress. Additionally, the state of depression was accompanied by an intensification of high-frequency EEG activity over the temporal regions. No significant differences in prefrontal alpha power asymmetry were observed under the described experimental conditions while comparing the states of calmness and emotional arousal of the participants for the three conditions of depression, anxiety, and stress.
Impact of patient-related factors on successful autologous fat injection laryngoplasty in thyroid surgical treated related unilateral vocal fold paralysis- observational study
(2020-01-01)
Lin, Wen-Yang
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Chang, Wen-Dien
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Ko, Li-Wei
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Tsou, Yung-An
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Chen, Sheng-Hwa
The aim of this study was to compare major voice indicators in different sub-categories, the outcome of lipoinjection for patients might be refined and some voice prognostic factors could be more particularized in specific sub-groups. This is an observational study, and sub-grouped UVFP patients into 3 categories: male vs female, BMI >= 24 vs BMI < 24, Age >= 60 vs Age < 60 for more detailed exploring whether sub-categories affected voice diagnostic and prognostic parameters. Patients' voice data is recorded before and after the autologous fat injection laryngoplasty by a multidimensional voice program. Overall, 73 patients' voice performance were improved 12 months later by vocal fold lipoinjection. In the comparison of the male with female revealed female obtained better Jita than male by surgery (Female: 174.50 +/- 100.58 Hz; Male: 294.82 +/- 253.65 Hz; P < .05). BMI >= 24 vs BMI < 24 showed no statistical difference. Patients aged under 60 demonstrated better Highest F0, lowest F0, NHR and ShdB than elder ones 12 months after receiving vocal fold lipoinjection. Thus, Noise-to-harmonics ratio (NHR), voice turbulence index (VTI), and ShdB (Absolute shimmer, dB) may be the major post-operative evaluating markers of patients' age under 60. Voice parameters showed no significant correlation with BMI. Female patients performed lower Jita (Absolute jitter, mu sec) than male patients 1 year after receiving treatment. The experimental results in this study showed UVFP patients' gender and age may stand as significant categories on analyzing clinical voice prognostic indicators, ShdB and Jita of autologous injection laryngoplasty.
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
;
交大名義發表
;
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.
Target Classification in a Novel SSVEP-RSVP Based BCI Gaming System
(2019-01-01)
Nayak, Tapsya
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Ko, Li-Wei
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Jung, Tzyy-Ping
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Huang, Yufei
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生物科技學系
Recently game-based brain-computer interface (BCI) systems using electroencephalography (EEG) has been gaining popularity, providing a sophisticated experience to its users. Here we present such a novel hybrid system based on rapid serial visual presentation (RSVP) in conjunction with steady-state visual evoked potentials (SSVEP). Based on a matching computer game Jewel Quest a game is designed wherein a sequence of jewel images containing rare targets (< 3%) in an RSVP paradigm is presented on a display at four distinct locations each flickering at different rates (4, 5, 6 and 7 Hz). A score is awarded upon successful detection of target image from neural signals. During real-time implementation to achieve higher classification speeds, EEG signals were epoched at the onset of each image, creating a high degree of class overlap and imbalance. Given these challenges in our EEG datasets, we present classifiers that can classify single-trial EEG epochs at the onset of target image presentation accurately. Initial results from 14 subjects indicate Hidden Markov Model (HMM) with Dirichlet emission probabilities provide similar to 1% higher, on average, the area under the precision-recall curve (AUC-PR) compared to the ensemble technique Bagging, commonly used to handle class imbalance.

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