學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 56

Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI
(2020-08-01)
Zhang, Hao-Yan
;
Stevenson, Cory E.
;
Jung, Tzyy-Ping
;
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.
Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task
(2020-06-01)
Ko, Li-Wei
;
Komarov, Oleksii
;
Lai, Wei-Kai
;
Liang, Wei-Gang
;
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.
Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
(2020-04-01)
Komarov, Oleksii
;
Ko, Li-Wei
;
Jung, Tzyy-Ping
;
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.
Target Classification in a Novel SSVEP-RSVP Based BCI Gaming System
(2019-01-01)
Nayak, Tapsya
;
Ko, Li-Wei
;
Jung, Tzyy-Ping
;
Huang, Yufei
;
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.
HARDWARE-ORIENTED MEMORY-LIMITED ONLINE FASTICA ALGORITHM AND HARDWARE ARCHITECTURE FOR SIGNAL SEPARATION
(2019-01-01)
Van, Lan-Da
;
Lu, Tsung-Che
;
Jung, Tzyy-Ping
;
Wang, Jo-Fu
;
資訊工程學系
This paper presents a hardware-oriented memory-limited online FastICA algorithm and its hardware architecture and implementation for eight-channel electroencephalogram ( EEG) signal separation. The online algorithm integrates the data overlapping, garbage detection, channel permutation, and momentum-controlled weight update schemes to stabilize the order of the decomposed source signals across time. This study also realizes the algorithm into a hardware architecture and implementation with a core area of 1.469x1.469 mm(2) in a TSMC 90 nm process. The resulting power dissipation for eight-channel EEG signal separation is 65 mW@100 MHz at 1V.
Eye fixation-related fronto-parietal neural network correlates of memory retrieval
(2019-04-01)
Tsai, Pei-Yi
;
She, Hsiao-Ching
;
Chen, Sheng-Chang
;
Huang, Li-Yu
;
Chou, Wen-Chi
Eye movements are considered to be informative with regard to the underlying cognitive processes of human beings. Previous studies have reported that eye movements are associated with which scientific concepts are retrieved correctly. Moreover, other studies have also suggested that eye movements involve the cooperative activity of the human brain's fronto-parietal circuits. Less research has been conducted to investigate whether fronto-parietal EEG oscillations are associated with the retrieval processing of scientific concepts. Our findings in this study demonstrated that the fronto-parietal network is indeed crucial for successful memory retrieval. In short, significantly lower theta augmentation in the frontal midline and lower alpha suppression in the right parietal region were observed at the 5th eye fixation for physics concepts that were correctly retrieved than for those that were incorrectly retrieved. Moreover, the visual cortex in the occipital lobe exhibits a significantly greater theta augmentation followed by an alpha suppression following each eye fixation, while a right frontoparietal asymmetry was also found for the successful retrieval of presentations of physics concepts. In particular, the study results showed that eye fixation-related frontal midline theta power and right parietal alpha power at the 5th eye fixation have the greatest predictive power regarding the correctness of the retrieval of physics concepts.
A brain-machine interface using dry-contact, low-noise EEG sensors
(2008-01-01)
Sullivan, Thomas J.
;
Deiss, Stephen R.
;
Jung, Tzyy-Ping
;
Cauwenberghs, Gert
;
交大名義發表
Electroencephalograph (EEG) recording systems offer a versatile, non-invasive window on the brain's spatiotemporal activity for many neuroscience and clinical applications. Our research aims to improve the convenience and mobility of EEG recording by eliminating the need for conductive gel and creating sensors that fit into a scalable array architecture. The EEG dry-contact electrodes are created with micro-electrical-mechanical system (MEMS) technology. Each channel of our analog signal processing front-end comes on a custom-built, dime-sized circuit board which contains an amplifier, filters, and analog-to-digital conversion. A daisy-chain configuration between boards with bit-serial output reduces the wiring needed. A system consisting of seven sensors is demonstrated in a real-world setting. Consuming just 3 mW, it is suitable for mobile applications. The system achieves an input-referred noise of 0.28 mu V-rms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. Noise behavior across the daisy-chain is characterized, alpha-band rhythms are detected, and an eye-blink study is demonstrated.
Resting State and Task-related Brain Dynamics Supporting Insight
(2014-01-01)
Wu, Ying Choon
;
Jung, Melody
;
Lock, Derrick
;
Chao, Eric
;
Swartz, Jerome
Problems can be solved in a variety of ways. One might systematically evaluate a known space of possible solutions until the right one is found. Alternatively, it may prove necessary to enlarge or restructure the expected problem space - so called "thinking outside the box." This approach can yield an experience of unexpected insight or feeling of Aha!. Current challenges to understanding this phenomenon from a neurocognitive perspective include the vast diversity of problem domains and time scales for solutions. Whereas the subjective suddenness of an "Aha!" moment may lead to the impression that insight must be precipitated by a set of discrete, short-lived neural events, this report outlines research revealing that even before a problem is presented, scalp-recorded measures of resting or baseline brain states are linked with future performance and likelihood of experiencing insight during the search for a solution. Additionally, this study also shows that compared to more systematic problem solving approaches, insight is accompanied by differences in cortical and likely cognitive engagement that are detectable throughout much of the problem solving phase, rather than being confined to a distinct interval immediately preceding the dawn of a solution. These findings are important for the development of therapies targeting problem solving and reasoning skills, such as those used in cognitive training interventions to mitigate the effects of cognitive decline.
The Wearable Multimodal Monitoring System: A Platform to Study Falls and Near-Falls in the Real-World
(2015-01-01)
Doty, Tracy Jill
;
Kellihan, Bret
;
Jung, Tzyy-Ping
;
Zao, John K.
;
Litvan, Irene
Falls are particularly detrimental and prevalent in the aging population. To diagnose the cause of a fall current medical practice relies on expensive hospital admissions with many bulky devices that only provide limited diagnostic information. By utilizing the latest wearable technology, the Wearable Multimodal Monitoring System (WMMS) presented here offers a better solution to the problem of fall diagnostics and has the potential to predict these falls in real-time in order to prevent falls or, at least, mitigate their severity. This highly integrated system has been designed for real-life long-term monitoring of movement disorder patients. It contains multiple wearable and wireless biosensors that simultaneously and continuously monitor cardiovascular, autonomic, motor, and electroencephalographic (EEG) activity, in addition to receiving critical patient feedback about symptoms. Initial pilot data show that the system is comfortable and easy to use, and provides high quality data streams capable of detecting near-falls and other motor disturbances.
Augmenting VR/AR Applications with EEG/EOG Monitoring and Oculo-Vestibular Recoupling
(2016-01-01)
Zao, John K.
;
Jung, Tzyy-Ping
;
Chang, Hung-Ming
;
Gan, Tchin-Tze
;
Wang, Yu-Te
Head-mounted virtual reality and augmented reality displays (a. k. a. VR/AR goggles) created a revolutionary multimedia genre that is seeking ever-broadening applications and novel natural human interfaces. Adding neuromonitoring and neurofeedback to this genre is expected to introduce a new dimension to user interaction with the cyber-world. This study presents the development of a Neuromonitoring VR/AR Goggle armed with electroencephalo- gram and electrooculogram sensors, programmable milli-Ampere current stimulators and wireless fog/cloud computing support. Beside of its potential use in mitigating cybersickness, this device may have potential applications in augmented cognition ranging from feedback-controlled perceptual training to on-line learning and virtual social interactions. A prototype of the device has been made from a Samsung Gear VR for S6. This study explains its technical design to ensure precision data sampling, synchronous event marking, real-time signal processing and big data cloud computing support. This study also demonstrates the effective-ness in measuring the event-related potentials during a visual oddball experiment.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results