Theresa Vaughan - Academia.edu (original) (raw)

Papers by Theresa Vaughan

AIP Conference Proceedings, 1992

We have measured a signal-to-noise of 37:1 at room temperature for 227 GeV pions at Fermilab on t... more We have measured a signal-to-noise of 37:1 at room temperature for 227 GeV pions at Fermilab on the n(ohmic)-side of a Hamamatsu AC-coupled double-sided silicon microstrip detector with 0.64 cm long strips. Position resolutions at normal incidence of 3.5+/-0.4 mum (10.4+/-0.5 mum) were obtained for the p-side (n-side) which had 25 mum (50 mum) pitch and 50 mum readout. The

Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology, Jul 1, 2009

Objective-Brain-computer interface (BCI) technology can provide severely disabled people with non... more Objective-Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6×6 P300 Speller.

2007 3rd International IEEE/EMBS Conference on Neural Engineering, 2007

A brain-computer interface (BCI) is a device that provides an alternate non-muscular communicatio... more A brain-computer interface (BCI) is a device that provides an alternate non-muscular communication/control channel for individuals with severe neuromuscular disabilities. The P300 event-related potential has been demonstrated to be a reliable signal for controlling a BCI. The ultimate goal is to continue to improve the classification speed and accuracy of a P300-based BCI. The method of common spatial patterns (CSP) has proven success with sensorimotor rhythm-based BCIs and, with some modifications, can also be used to accurately classify the P300. The present method, Common Spatio-Temporal Patterns (CSTP), extends CSP by incorporating time-delay embedding to extract the prominent spatio-temporal patterns corresponding to each class. The results indicate that CSTP is capable of identifying a decomposition subspace that accurately classifies the P300. In addition, this subspace can be visualized to provide useful insight regarding the discriminable spatio-temporal characteristics of the P300.

Lecture Notes in Computer Science, 2011

Great things can be achieved even with very low bandwidth. Stephen Hawking has been able to break... more Great things can be achieved even with very low bandwidth. Stephen Hawking has been able to break new ground in theoretical physics just by twitching his hand and cheek. Jean-Dominique Bauby was able to write a bestselling memoir by blinking one eyelid. By reading and decoding "brain-waves", the field of brain-computer interfacing (BCI) is poised to open up the possibility of such expression, even for people who can no longer move a single muscle. A BCI still requires an HCI front-end to be of practical use, but many currentlyused HCIs do not adequately address limitations on the typical target user's input (e.g., limited eye movement leading to poor spatial vision) or output (e.g. variable delays, and false positives/negatives, in "pressing the button"). In this symposium, BCI experts will present their view of the challenges arising from these limitations. The HCI community is invited to participate in a competition to provide the best solutions.

The Frontiers Collection, 2009

E.W. Sellers et al.

Archives of Physical Medicine and Rehabilitation, 2015

Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brai... more Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2003

This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second Int... more This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encode...

Neurology, 2005

People with severe motor disabilities can maintain an acceptable quality of life if they can comm... more People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

Journal of Neuroscience Methods, 2008

This study examines the effects of expanding the classical P300 feature space on the classificati... more This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm . Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline for not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.

Journal of Neural Engineering, 2006

This study assesses the relative performance characteristics of five established classification t... more This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin [5]. Four linear methods: Pearson's correlation method (PCM), Fisher's Linear Discriminant (FLD), stepwise linear discriminant analysis (SWLDA), and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.

IEEE Transactions on Biomedical Engineering, 2004

Interest in developing a new method of man-tomachine communication-a brain-computer interface or ... more Interest in developing a new method of man-tomachine communication-a brain-computer interface or BCIhas grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools.

Clinical Neurophysiology, 2005

Objective: People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the el... more Objective: People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.

Clinical Neurophysiology, 2002

For many years people have speculated that electroencephalographic activity or other electrophysi... more For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world -a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances. q

Biological Psychology, 2006

We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The B... more We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 Â 3 or 6 Â 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 Â 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 Â 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 Â 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication. #

Biological Psychology, 2009

The scanning protocol is a novel brain-computer interface (BCI) implementation that can be contro... more The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.

Amyotrophic Lateral Sclerosis, 2010

Our objective was to develop and validate a new brain-computer interface (BCI) system suitable fo... more Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% ( r ϭ -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this fi rst user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute signifi cantly to quality of life and productivity.