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63
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Introduction
My research focuses on development of advanced brain-machine interfaces (BMI/BCI), clinical diagnostics/therapeutics, and brain/body state decoding using neuroscience principles, signal processing, and statistical machine learning with multiple sensor modalities (M/EEG, implants, NIRS, eye tracking, peripheral sensors, etc). I also have an interest in ubiquitous computing, human-computer interaction and the intersection of these fields and cognitive neuroscience and neural interface technology.
Additional affiliations
September 2009 - present
September 2008 - present
June 2006 - March 2007
Education
August 2004 - April 2008
Publications
Publications (63)
Because of the increasing portability and wearability of noninvasive electrophysiological systems that record
and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent,
and response to events are of increasing interest. Brain–
computer interface (BCI) systems can make use of such
kn...
Goal:
We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.
Methods:
The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive arti...
We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGL...
In this chapter, the historical context and relevant scientific, artistic, and cultural milieus from which the idea of brain-computer interfaces involving multiple participants emerged is discussed. Additional contextualization includes descriptions of the intellectual climate from which ideas about brain biofeedback led to pioneering applications...
OBJECTIVES
This document aims at providing an overview of the existing and developing standards in the field of neurotechnologies for brain‐machine interfacing. It is mainly focused on systems that provide a
closed‐loop interaction with artificial devices based on information extracted from measures of the activity in the nervous systems.
In additi...
Introduction
Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an f...
Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of in...
In this proof-of-concept study involving expert Vipassana practitioners (n=34), we successfully decoded self-reported meditative depth using source-localized EEG activity in the theta, alpha, and gamma bands, revealing a remarkable accuracy in predicting these gradations in unseen sessions across two separate visits. Our finding suggests that neith...
With the emergence of numerous brain computer interfaces (BCI), their form factors, and clinical applications the terminology to describe their clinical deployment and the associated risk has been vague. The terms “minimally invasive” or “non-invasive” have been commonly used, but the risk can vary widely based on the form factor and anatomic locat...
Mental overload and mental fatigue are two degraded cognitive states that are known to promote cognitive incapacitation. We adopted a neuroergonomics approach to investigate these states that remain difficult to induce under laboratory settings thus impeding their measurement. Two experiments were conducted under real flight conditions to respectiv...
Although several guidelines for best practices in EEG preprocessing have been released, even those studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing meth...
Mental overload and mental fatigue are two degraded cognitive states that are known to promote cognitive incapacitation. We adopted a neu-roergonomics approach to investigate these states that remain difficult to induce under laboratory settings thus impeding their measurement. Two experiments were conducted under real flight conditions to respecti...
EEG preprocessing approaches have not been standardized, and even those studies that follow best practices contain variations in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to preprocessing methods and parameters. To address this issue, we analyze the effect of...
Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if...
Accident analyses have revealed that pilots can
fail to process auditory stimuli such as alarms, a phenomenon
known as inattentional deafness. The motivation of this research
is to develop a passive brain computer interface that can
predict the occurence of this critical phenomenon during real
flight conditions. Ten volunteers, equipped with a dry-...
We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigm...
In this paper, we present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experi...
Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the combination of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if suc...
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuri...
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brai...
A pilot system for performing real-time brain imaging in naturalistic environments have been developed using wireless EEG headsets, motion sensors, smart telephones and ubiquitous computing servers. This paper described its pervasive architecture and introduced its enabling technologies, which include machine-to-machine publish/subscribe protocols,...
Conventional neuroimaging analyses have revealed the computational specificity of localized brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a...
Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory moda...
Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector...
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...
The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocess...
Single- and multi-agent installations and performances that use physiological signals to establish an interface between music and mental states can be found as early as the mid-1960s. Among these works, many have used physiological signals (or inferred cognitive, sensorimotor or affective states) as media for music generation and creative expressio...
Here we demonstrate that the activity of neural ensembles can be
quantitatively modeled. We first show that an ensemble dynamical model (EDM)
accurately approximates the distribution of voltages and average firing rate
per neuron of a population of simulated integrate-and-fire neurons. EDMs are
high-dimensional nonlinear dynamical models. To facili...
A demo MATLAB code for Online Recursive Independent Component Analysis (ORICA).
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...
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...
An augmented brain computer interface that can detect users' brain states in real-life situations has been developed using wireless EEG headsets, smart phones and ubiquitous computing services. This kind of wearable natural user interfaces will have a wide-range of potential applications in future smart environments. This paper describes its ubiqui...
EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advance...
Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvem...
Freezing of gait (FOG) is an elusive phenomenon that debilitates a large number of Parkinson’s disease (PD) patients regardless of stage of disease, medication status, or deep brain stimulation implantation. Sensory feedback cues, especially visual feedback cues, have been shown to alleviate FOG episodes or even prevent episodes from occurring. Her...
The planning of goal-directed movement towards targets in different parts of space is an important function of the brain. Such visuo-motor planning and execution is known to involve multiple brain regions, including visual, parietal, and frontal cortices. To understand how these brain regions work together to both plan and execute goal-directed mov...
“The mind is the music that neural networks play.” This quote from computational neurobiologist T.J. Sejnowski underscores a growing scientific consensus that studying the structure and function of vast networks of connections between brain regions is essential to understanding cognitive and affective state maintenance, sensorimotor information pro...
This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a p...
We developed a toolbox for detecting high-frequency oscillations and evaluating cross-frequency phase-amplitude coupling in electrocorticographic (ECoG) data with optimal parameters. Here we demonstrate use of the toolbox using simulated and realistic ECoG data. The results confirmed its potential usefulness for clinical research or practice. The t...
A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the...
This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization within the SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG...
Mapping the dynamics of neural source processes critically involved in initiating and propagating seizure activity is important for effective epilepsy diagnosis, intervention, and treatment. Tracking time-varying shifts in the oscillation modes of an evolving seizure may be useful for both seizure onset detection as well as for improved non-surgica...
Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models a...
Development of EEG-based brain computer interface (BCI) methods has largely focused on creating a communication channel for subjects with intact cognition but profound loss of motor control from stroke or neurodegenerative disease that allows such subjects to communicate by spelling out words on a personal computer. However, other important human c...
This is the Theoretical Handbook and User Manual for the Source Information Flow Toolbox. Written in partial fulfillment of the Master of Science degree in Cognitive Science
Development of EEG-based brain computer interface (BCI) methods has largely focused on creating a communication channel for subjects with intact cognition but profound loss of motor control from stroke or neurodegenerative disease that allows such subjects to communicate by spelling out words on a personal computer. However, other important human c...
Inferring a user's cognitive state and behavior is a goal actively sought in the field of Human-Computer Interaction (HCI). Although Brain-Computer Interface (BCI) technology may contribute substantially to this endeavor, cost and wearability, as well as developing a cohesive framework for multi-application BCI, remain critical factors in extending...
Development of EEG-based brain computer interface (BCI) methods has largely focused on creating a communication channel for subjects with intact cognition but profound loss of motor control from stroke or neurodegenerative disease, allowing such subjects to communicate by spelling out words on a personal computer. However, another important human c...
An interesting issue in cognitive neuroscience is discovering the spatial distribution of cortical areas engaged in specific task-dependent information processing. We may also like to know whether any these (perhaps distant) cortical areas are functionally linked, constituting a spatially-fixed, distributed processing network. A method that groups...
A significant challenge in contemporary neuroscience lies in estimating and visualizing the time-and frequency-dependent dynamics of information flow within distributed anatomical networks and relating these dynamics to cognitive phenomena. While scalp EEG affords high temporal resolution, the traditional approach of estimating connectivity between...