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27
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Introduction
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September 2012 - January 2017
Publications
Publications (27)
Independent component analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and v...
The Electroencephalogram (EEG) is a noninvasive functional brain activity recording method that shows promise for becoming a 3-D cortical imaging modality with high temporal resolution. Currently, most of the tools developed for EEG analysis focus mainly on offline processing. This study introduces and demonstrates the Real-time EEG Source-mapping...
Objective:
As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different...
Advancing our understanding of neurocognitive systems impacted by hypnotherapy may improve therapeutic outcomes. This study addresses the challenge of decoding cortical state changes from continuous electroencephalographic (EEG) data recorded during hypnosis. We model changes in brain state dynamics over the course of hypnosis using Adaptive Mixtur...
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decompositi...
Here we assume that emotional states correspond to functional dynamic states of brain and body, and attempt to characterize the appearance of these states in high-density scalp electroencephalographic (EEG) recordings acquired from 31 participants during 1-2 hour sessions, each including fifteen 3-5 min periods of self-induced emotion imagination u...
Schizophrenia is a debilitating mental disorder that is associated with cognitive deficits. Impairments in cognition occur early in the course of illness and are associated with poor functional outcome, but have been difficult to treat with conventional treatments. Recent studies have implicated abnormal neural network dynamics and impaired connect...
Background:
The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain-computer interface (BCI) with virtual reality (VR) has develop...
Objective:
Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported,...
Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain....
When recording neural activity from extracellular electrodes, spike sorting is needed to separate the activity of different neurons. Most of the spike sorting packages are offline and use all the available data to distinguish the activity of single neurons from the recordings. However, when performing an experiment, it is helpful to monitor the act...
Non-brain contributions to electroencephalo-graphic (EEG) signals, often referred to as artifacts, can hamper the analysis of scalp EEG recordings. This is especially true when artifacts have large amplitudes (e.g., movement artifacts), or occur continuously (like eye-movement artifacts). Offline automated pipelines can detect and reduce artifact i...
One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts-non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and real-time capable, component-based method that can effectively re...
As the human brain performs cognitive functions or generates spontaneous mental processes within ever-changing, real-world environments, states of the brain are inevitably nonstationary. This calls for innovative approaches to both obtain objective and quantitative insights into hidden cognitive and mental states and study the dynamics of brain sta...
This study proposes a new algorithm to detect steady-state visual evoked potentials (SSVEPs) based on a template-matching approach combined with independent component analysis (ICA)-based spatial filtering. In recent studies, the effectiveness of the template-based SSVEP detection has been demonstrated in a high-speed brain-computer interface (BCI)...
In real-life situations, where humans optimize their behaviors to effectively interact with unknown and dynamic environments, their brain activities are inevitably nonstationary. Electroencephalogram (EEG), a widely used neuroimaging modality, has a high temporal resolution for characterizing the brain nonstationarity. However, quantitative measure...
Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However...
Electroencephalography (EEG)-based emotion classification has drawn increasing attention over the last few years and become an emerging direction in brain-computer interfaces (BCI), namely affective BCI (ABCI). Many prior studies devoted to improve emotion-classification models using the data collected within a single session or day. Less attention...
The needs for online Independent Component Analysis (ICA) algorithms arise in a range of fields such as continuous clinical assessment and brain-computer interface (BCI). Among the online ICA methods, online recursive ICA algorithm (ORICA) has attractive properties of fast convergence and low computational complexity. However, there hasn't been a s...
A demo MATLAB code for Online Recursive Independent Component Analysis (ORICA).
Online Independent Component Analysis (ICA) algorithms have recently seen increasing development and application across a range of fields, including communications, biosignal processing, and brain-computer interfaces. However, prior work in this domain has primarily focused on algorithmic proofs of convergence, with application limited to small `to...
The ability to evaluate the cerebral microvascular structure and function is crucial for investigating pathological processes in brain disorders. Previous angiographic methods based on blood oxygen level-dependent (BOLD) contrast offer appropriate visualization of the cerebral vasculature, but these methods remain to be optimized in order to extrac...
Projects
Projects (4)
Study the effect of neuromodulation in patients with neurological disorders. Develop brain decoding algorithms for monitoring mental health.
[Demo Video: Real-time Automatic Artifact Rejection using REST: https://www.youtube.com/watch?v=N3N-rHWexIs]
1. Development of Real-time EEG Source-mapping Toolbox (REST) (Github: https://github.com/goodshawn12/REST) for online adaptive source separation, source classification, and artifact removal in near real-time.
2. Evaluation and validation of Artifact Subspace Reconstruction (ASR) as an automatic online-capable artifact removal method.
1. Present computational models for effective and robust decoding of brain states from nonstationary biosignals.
2. Propose quantitative measures for assessing and tracking state changes.
3. Develop data-driven (unsupervised learning) approaches for addressing issues with unlabeled, continuous data
4. Develop hypothesis-driven approaches for neurophysiological interpretations and scientific insights.
