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HARDWARE-ORIENTED MEMORY-LIMITED ONLINE FASTICA ALGORITHM AND HARDWARE ARCHITECTURE FOR SIGNAL SEPARATION
(2019-01-01)
Van, Lan-Da
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Lu, Tsung-Che
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Jung, Tzyy-Ping
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Wang, Jo-Fu
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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.
Toward a new cognitive neuroscience: modeling natural brain dynamics
(2014-06-19)
Gramann, Klaus
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Jung, Tzyy-Ping
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Ferris, Daniel P.
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Lin, Chin-Teng
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Makeig, Scott
Sustained Attention in Real Classroom Settings: An EEG Study
(2017-07-31)
Ko, Li-Wei
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Komarov, Oleksii
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Hairston, W. David
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Jung, Tzyy-Ping
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Lin, Chin-Teng
Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.
Predicting EEG Sample Size Required for Classification Calibration
(2016-01-01)
Mao, Zijing
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Jung, Tzyy-Ping
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Lin, Chin-Teng
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Huang, Yufei
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腦科學研究中心
This study considers an important problem of predicting required calibration sample size for electroencephalogram (EEG)-based classification in brain computer interaction (BCI). We propose an adaptive algorithm based on learning curve fitting to learn the relationship between sample size and classification performance for each individual subject. The algorithm can always provide the predicted result in advance of reaching the baseline performance with an average error of 17.4 %. By comparing the learning curve of different classifiers, the algorithm can also recommend the best classifier for a BCI application. The algorithm also learns a sample size upper bound from the prior datasets and uses it to detect subject outliers that potentially need excessive amount of calibration data. The algorithm is applied to three EEG-based BCI datasets to demonstrate its utility and efficacy. A Matlab package with GUI is also developed and available for downloading at https://github.com/ZijingMao/LearningCurveFittingForSampleSizePrediction. Since few algorithms are yet available to predict performance for BCIs, our algorithm will be an important tool for real-life BCI applications.
Kinesthesia in a sustained-attention driving task
(2014-05-01)
Chuang, Chun-Hsiang
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Ko, Li-Wei
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Jung, Tzyy-Ping
;
Lin, Chin-Teng
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生物科技學系
This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. (C) 2014 Elsevier Inc. All rights reserved.
Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data
(2018-11-12)
Fonseca, Andre
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Kerick, Scott
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King, Jung-Tai
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Lin, Chin-Teng
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Jung, Tzyy-Ping
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
An EEG-Based Fatigue Detection and Mitigation System
(2016-06)
Huang, Kuan-Chih
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Huang, Teng-Yi
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Chuang, Chun-Hsiang
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King, Jung-Tai
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Wang, Yu-Kai
Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual\'s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant\'s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject\'s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
MoBI-Box: A Next Generation Mobile Brain-Body Imaging Platform
(2016)
Wu, Ying Choon
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Wang, Jeffery
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Tran, Andrew
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Schperberg, Alex
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Caldwell, John
MoBI-Box is an inexpensive, portable, and ergonomic desktop system for simultaneously measuring and integrating multiple types of biosignals that transpire at different time scales and levels of organization. It can be assembled from readily available, off-the-shelf components and customized to suit specific needs. It can be implemented in a wide range of settings, including homes, classrooms, clinics, and research labs for only a modest start-up cost (beginning around $1200). MoBI-Box systems are currently in use for a number of training and research applications. This paper will outline an ongoing project to explore the impact of motor imagery on learning of a complex skill with both motoric and conceptual elements namely, learning Mandarin orthography. Findings have implications for the development of training systems in other specialized domains that require not only kinesthetic expertise, but also specialized knowledge.
Selective Transfer Learning for EEG-Based Drowsiness Detection
(2015-01-01)
Wei, Chun-Shu
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Lin, Yuan-Pin
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Wang, Yu-Te
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Jung, Tzyy-Ping
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Bigdely-Shamlo, Nima
On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject\'s pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual\'s pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects\' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.
Tonic and phasic EEG and behavioral changes induced by arousing feedback
(2010-08-15)
Lin, Chin-Teng
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Huang, Kuan-Chih
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Chao, Chih-Feng
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Chen, Jian-Ann
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Chiu, Tzai-Wen
This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha- and theta-band spectra of a bilateral occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta- and alpha-bands of the bilateral occipital component. The effects of auditory feedback on spectral changes lasted 30 s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges. (C) 2010 Elsevier Inc. All rights reserved.

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