WorldCat Identities

Tzyy-Ping Jung

Works: 18 works in 27 publications in 1 language and 161 library holdings
Genres: Academic theses 
Roles: Contributor, Author, Other
Classifications: QP376.6, 616.8
Publication Timeline
Most widely held works by Tzyy-Ping Jung
Towards a New Cognitive Neuroscience: Modeling Natural Brain Dynamics by Scott Makeig( )

3 editions published in 2014 in English and Undetermined and held by 129 WorldCat member libraries worldwide

Decades of brain imaging experiments have revealed important insights into the architecture of the human brain and the detailed anatomic basis for the neural dynamics supporting human cognition. However, technical restrictions of traditional brain imaging approaches including functional magnetic resonance tomography (fMRI), positron emission tomography (PET), and magnetoencephalography (MEG) severely limit participants' movements during experiments. As a consequence, our knowledge of the neural basis of human cognition is rooted in a dissociation of human cognition from what is arguably its foremost, and certainly its evolutionarily most determinant function, organizing our behavior so as to optimize its consequences in our complex, multi-scale, and ever-changing environment. The concept of natural cognition, therefore, should not be separated from our fundamental experience and role as embodied agents acting in a complex, partly unpredictable world. To gain new insights into the brain dynamics supporting natural cognition, we must overcome restrictions of traditional brain imaging technology. First, the sensors used must be lightweight and mobile to allow monitoring of brain activity during free participant movements. New hardware technology for electroencephalography (EEG) and near infrared spectroscopy (NIRS) allows recording electrical and hemodynamic brain activity while participants are freely moving. New data-driven analysis approaches must allow separation of signals arriving at the sensors from the brain and from non-brain sources (neck muscles, eyes, heart, the electrical environment, etc.). Independent component analysis (ICA) and related blind source separation methods allow separation of brain activity from non-brain activity from data recorded during experimental paradigms that stimulate natural cognition. Imaging the precisely timed, distributed brain dynamics that support all forms of our motivated actions and interactions in both laboratory and real-world settings requires new modes of data capture and of data processing. Synchronously recording participants' motor behavior, brain activity, and other physiology, as well as their physical environment and external events may be termed mobile brain/body imaging ('MoBI'). Joint multi-stream analysis of recorded MoBI data is a major conceptual, mathematical, and data processing challenge. This Research Topic is one result of the first international MoBI meeting in Delmenhorst Germany in
An algorithm for deriving an articulatory-phonetic representation by Tzyy-Ping Jung( )

3 editions published in 1993 in English and held by 5 WorldCat member libraries worldwide

In this research work we investigate a new articulatory-phonetic representation, called the gestural score, as a model of human speech production and perception. This dissertation discusses three types of analyses which have been used to produce gestural scores for various consonant-vowel-consonant syllables. First, we describe an algorithm which nonlinearly transforms the recorded positional values of four pellets placed on the tongue during articulation into variables in a new Cartesian space, in which the new x- and y-values represent the distance of the pellets going back along the opposing vocal tract wall and the distance perpendicular to the tract wall. Second, the transformed articulatory data serve as input for a principal components analysis of four tongue pellets. The objectives of this analysis are twofold: (1) to examine the effectiveness of the coordinate transformation, and (2) to study the constriction features of vowels. Third, a computational model which performs spatio-temporal mapping from articulatory data to the gestural score representation is examined. The computational model uses temporal decomposition to approximate multi-channel trajectories and yield a set of target functions and vectors. Temporal decomposition constructs multiple-channel trajectories from data-derived target functions using a form of adaptive Gauss-Seidel iteration. The resultant target functions, in conjunction with the weights for each basis function, are then used to derive the articulatory-phonetic representation--gestural scores for the CVC syllables embedded in the frame sentences. The voicing information is derived from the simultaneously recorded electroglottograph data. This method is applied to the task of estimating the gestural score for various CVC syllables in a stimulus set. To evaluate the adequacy of the derived gestural scores two evaluations were performed: a perception test, and a classification test using an automatic recognizer based on a neural network model. High recognition rates from both the perceptual experiments and the automatic recognizers support the hypothesis that sufficient information is available in the resultant gestural scores to allow accurate identification of the phonetic elements
Closed-Loop Brain-Machine-Body Interfaces for Noninvasive Rehabilitation of Movement Disorders by Frédéric D Broccard( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

Analysis of fMRI Data by Blind Separation into Independent Spatial Components( Book )

2 editions published in 1997 in English and held by 2 WorldCat member libraries worldwide

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured brain electrical signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski. We decomposed eight fMRI data sets from 4 normal subjects performing various cognitive tasks. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and related fourth-order decomposition technique were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations
Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis by Yuan-Pin Lin( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

Functionally Independent Components of the Late Positive Event-Related Potential During Visual Spatial Attention( Book )

2 editions published in 1999 in English and held by 2 WorldCat member libraries worldwide

Human event-related potentials (ERPs) were recorded from 10 subjects presented with visual target and nontarget stimuli at five screen locations and responding to targets presented at one of the locations. The late positive response complexes of 25-75 ERP average waveforms from the two task conditions were simultaneously analyzed with independent Component Analysis, a new computational method for blindly separating linearly mixed signals. Three spatially fixed, temporarily independent, behaviorally relevant, and physiologically plausible components were identified without reference to peeks in single-channel waveforms
Estimating Driving Performance Based on EEG Spectrum Analysis by C. T Lin( )

1 edition published in 2005 in English and held by 2 WorldCat member libraries worldwide

Removing Electroencephalographic Artifacts by Blind Source Separation( Book )

2 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic ẼEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic ẼEG! recordings to derive parameters characterizing the appearance and spread of EEG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis ̃PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis ĨCA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity
Combined Eye Activity Measures Accurately Estimate Changes in Sustained Visual Task Performance( Book )

2 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide

Five concurrent eye activity measures were used to model fatigue-related changes in performance during a visual compensatory tracking task. Nine participants demonstrated considerable variations in performance level during two 53-min testing sessions in which continuous video-based eye activity measures were obtained. Using a trackball, participants were required to maneuver a target disk (destabilized by pseudorandom wind forces) within the center of an annulus on a CRT display. Mean tracking performance as a function of time across 18 sessions demonstrated a monotonic increase in error from 0 to 11 min, and a performance plateau thereafter. Individual performance fluctuated widely around this trend with an average root mean square (RMS) error of 2.3 disk radii. For each participant, moving estimates of blink duration and frequency, fixation dwell time and frequency, and mean pupil diameter were analyzed using non-linear regression and artificial neural network techniques. Individual models were derived using eye and performance data from one session and cross-validated on data from a second session run on a different day. Results suggest that information from multiple eye measures may be combined to produce accurate individualized real-time estimates of sub-minute scale performance changes during sustained tasks
Blind Separation of Auditory Event-Related Brain Responses into Independent Components( Book )

2 editions published in 1997 in English and held by 2 WorldCat member libraries worldwide

Average event-related potential (ERP) data recorded from the human scalp reveal electro-encephalo- graphic EEG) activity that is reliable time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected- target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states
Editorial Message: Special Issue on Fuzzy Brain-Computer Interface Systems by Yi-Hung Liu( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach by Nikhil R Pal( )

1 edition published in 2008 in English and held by 2 WorldCat member libraries worldwide

Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset by Yuan-Pin Lin( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

Blind Separation of Event-Related Brain Responses into Independent Components( )

1 edition published in 1996 in English and held by 1 WorldCat member library worldwide

Functional imaging of brain activity based on changes in blood flow does not supply information about the relative timing of brief bursts of neural activity in different brain areas. Multichannel electric or magnetic recordings from the scalp provide high temporal resolution, but are not easily decomposed into the separate activities of multiple brain networks. We report here a method for the blind separation of event-related brain responses into spatially stationary and temporally independent subcomponents using an Independent Component Analysis algorithm. Applied to electroencephalographic responses from an auditory detection task, each of the most active identified sources accounted for all or part of a previously identified response component. This spatiotemporal decomposition was robust to changes in sensors and input data length, and was stable within subjects. The method can be used to assess the timing, strength, and stability of event-related activity in brain networks during cognitive tasks, regardless of source location
Tonic, Phasic, and Transient EEG Correlates of Auditory Awareness in Drowsiness( )

1 edition published in 1995 in English and held by 1 WorldCat member library worldwide

During drowsiness, human perfoimance in responding to above-threshold auditory targets tends to vary irregularly over periods of 4 min and longer. These pefformance fluctuations are accompanied by distinct changes in the frequency spectrum of the electroencephalo- gram (EEG) on three time scales: (I) during minute-scale and longer periods of intermittent responding mean activity levels in the (<4 Hz) delta and (4-6 Hz) fl%eta bands and at the sleep spindle frequency (14 Hz) are higher than during alert performance. (2) In most subjects, 4-6 Hz theta EEG activity begins to increase and gamma band activity above 35 Hz hegins to decrease, about 10 5 hefore pr-sentations of undetected targets, while hefore detected targets, 4-6 Hz amplitude decreases and gamma hand amplitude increases. Both these amplitude differences last 15-20 5 and occur in parallel with event-related cycles in target detection prohahility. In the same periods, alpha and sleep-spindle frequency amplitudes also show prominent 15-20 5 cycles, but these are not phase locked to pefformance cycles. (3) A second or longer after undetected targets, amplitude at intermediate (10-25 Hz) frequencies decreases hriefly, while detected targets are followed by a transient amplitude increase in the same latency and frequency range
BioCAS 2012 special issue [... 2012 IEEE Biomedical Circuits and Systems Conference, thas was held November 28-30, 2012 ... Hsinchu, Taiwan]( Book )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Pattern recognition using neural networks by Tzyy-Ping Jung( )

1 edition published in 1989 in English and held by 1 WorldCat member library worldwide

14.4: Polychromatic High-Frequency Steady-State Visual Evoked Potentials for Brain-Display Interaction( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Abstract: An oncoming interactive platform integrated with LCD provides another option not only for handicapped but public to make our daily lives more convenient. Steady-state visual evoked potentials (SSVEPs) are human brain responses to visual stimulation at specific light flashing frequencies. With the improvement of brain-computer interface (BCI) applications in mind, this paper proposed effective and comfortable SSVEP stimuli of high-frequency and polychromatic encoded lights with tunable duty cycles and phases
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English (26)