Dennis Mcfarland

Wadsworth Center, NYS Department of Health · Laboratory for Neural Injury and Repair

Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. BCI technology might also be able to enhance rehabilitation of motor function. We have previously shown that pre-movement sensorimotor rhythm (SMR) amplitude affects reaction time and performance on a joystick-based cursor movement...

The present review summarizes the early development of theories and testing of mental abilities with a particular emphasis on issues related to general and specific abilities. Any such review necessarily omits a vast amount of material. Contributions of certain individuals are covered but it should be kept in mind that their work was in the context...

This study examined the complexity of the Delis–Kaplan Executive Function System both in terms of the overall number of factors needed to model the entire battery and the complexity of the number of factors needed to model individual tests. The correlations between tests from the Delis–Kaplan Executive Function System standardization sample were mo...

Recent work has demonstrated that P300-based BCI systems can provide long-term communication for individuals with amyotrophic lateral sclerosis (ALS). However, even individuals with a successful history of BCI home use, can experience substantial variation in their day-today P300-based BCI performance. Recent studies suggest that functional connect...

Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assist...

Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. BCI technology may also be able to enhance rehabilitation of motor function (Lancet Neurology 7:1032-43, 2008). Toward this end, we seek to find features of cortico-muscular coupling that individuals might learn to control through f...

Objective. Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on r...

People can learn over training sessions to increase or decrease sensorimotor rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investiga...

A brain–computer interface (BCI) is a computer-based system that acquires, analyzes, and translates brain signals into output commands in real time. Perdikis and colleagues demonstrate superior performance in a Cybathlon BCI race using a system based on “three pillars”: machine learning, user training, and application. These results highlight the f...

Objective: To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months. Methods: Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine...

Introduction: Robotic devices, including exoskeletons and brain-computer interface (BCI) technology are attracting increasing interest as tools for enhancing movement training after stroke [1]. To the extent that poor motor preparation limits motor function, using a BCI to train pre-movement sensorimotor rhythms (SMR) might improve the ensuing moto...

Brain–Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed....

Objective: The present study examined the extent to which the covariance structure of the WAIS-IV is best accounted for by models that assume that test performance is the result of group-level factors and multiple independent general factors. Method: Structural models with one to four general factors were evaluated with either four or five group...

Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. There has been speculation that this technology might also be useful for a variety of diverse therapeutic applications. This survey considers possible ways that BCI technology can be applied to motor rehabilitation following stroke,...

Given the frequency of naming errors in aphasia, a common aim of speech and language rehabilitation is the improvement of naming. Based on evidence of significant word recall improvements in patients with memory impairments, errorless learning methods have been successfully applied to naming therapy in aphasia; however, other evidence suggests that...

Phase-locking value (PLV) is a potentially useful feature in sensorimotor rhythm-based brain–computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but i...

Objective: Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) res...

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through...

Theories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems...

BCI-based sensorimotor rhythm training can affect individuated finger movements D.J. McFarland1, S.L. Norman2, W.A. Sarnacki1, E.T. Wolbrecht3, D.J. Reinkensmeyer2, J.R. Wolpaw1 1 Wadsworth Center, Albany, NY, USA; 2University of California Irvine, Irvine, CA, USA; 3University of Idaho, Moscow, ID, USA Brain-computer interface (BCI) technology can...

http://castor.tugraz.at/doku/BCIMeeting2016/Proceedings_Meeting_2016.pdf DOI: 10.3217/978-3-85125-467-9

Brain-computer interfaces are systems that use signals recorded from the brain to enable communication and control applications for individuals who have impaired function. This technology has developed to the point that it is now being used by individuals who can actually benefit from it. However, there are several outstanding issues that prevent w...

Objective: Brain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-moveme...

The present study examined issues related to structural modeling of abilities by the use of simulated data as well as analysis of the standardization data from the Woodcock-Johnson-III. In both cases, results were evaluated with cross-validation. Simulation results showed that cross-validation with an independent data set was more successful in ide...

Objective: In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach: This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based...

Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facili...

Spanning almost 6 decades, CAPD, defined as a modality specific perceptual dysfunction not due to peripheral hearing loss, still remains controversial and requires further development if it is to become a useful clinical entity. Early attempts to quantify the effects of central auditory nervous system lesions based on the use of filtered-speech mat...

Motivated by P300 spelling scenarios involving communication based on a limited vo-cabulary, we propose a probabilistic graphical model-based framework and an associated classification algorithm that uses learned statistical prior models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate...

Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the pres...

Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize te...

Purpose: Factor analysis is a useful technique to aid in organizing multivariate data characterizing speech, language, and auditory abilities. However, knowledge of the limitations of factor analysis is essential for proper interpretation of results. The present study used simulated test scores to illustrate some characteristics of factor analysis...

Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors aff...

Performance on a cognitive test can be viewed either as measuring a unitary function or as reflecting the operation of multiple factors. Individual subtests in batteries designed to measure human abilities are commonly modeled as a single latent factor. Several latent factors are then used to model groups of subtests. However these latent factors a...

The temporal development of behavioral effects due to central nervous system infection with the herpes simplex type 1 virus was examined in two mouse models. Following infection of adult female NYA/Nylar mice with the HF strain of herpes, or adult female BALB/c mice with the F strain, the majority of animals survived. An increase in motor activity...

To assess the reliability of broadband middle ear power reflectance (BMEPR) and transmittance profiles for chirp and tonal stimuli using Generalizability Theory (GT). In adults without a history of middle-ear disease, we assessed the reliability of BMEPR to chirp and tonal stimuli using a multivariate approach based on an ANOVA model (GT). For comp...

Background: Tests of auditory perception, such as those used in the assessment of central auditory processing disorders ([C]APDs), represent a domain in audiological assessment where measurement of this theoretical construct is often confounded by nonauditory abilities due to methodological shortcomings. These confounds include the effects of cogn...

OBJECTIVE: Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. METHODS: Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivari...

The influence of age at infection on the nature of the behavioral effects of herpes simplex type 1 encephalitis in mice was examined. Weanling and adult mice were initially immunized with a footpad (FP) inoculation of virus, followed 2 weeks later by an intracerebral (IC) inoculation. Subsequent open-field testing 2 weeks later revealed that weanli...

Objective: Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and oth...

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predic...

In the last 15 years, a recognizable surge in the field of Brain Computer Interface (BCI) research and development has emerged. This emergence has sprung from a variety of factors. For one, inexpensive computer hardware and software is now available and can support the complex high-speed analyses of brain activity that is essential is BCI. Another...

The purpose of a brain-computer interface (BCI) is to detect and quantify characteristics of brain signals that indicate what the user wants the BCI to do, to translate these measurements in real time into the desired device commands, and to provide concurrent feedback to the user. The brainsignal characteristics used for this purpose are called si...

This chapter discusses the kinds of translation algorithms most frequently used in brain-computer interfaces (BCIs). It is organized into four sections. The first section considers the factors important in selecting a model and provides an overview of the models used in BCI translation algorithms. The second and third sections discuss the two other...

In the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a sin...

Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recen...

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offlin...

This third chapter discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. The authors also present a framework, building on the computation, anatomy, and physiology (CAP) model, for incorporating some of the principles discussed in the 2 previous chapters by Frey et al and Sathian et al in the pr...

Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have als...

The brain's electrical signals enable people without muscle control to physically interact with the world.

Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect mo...

BCI research has demonstrated the feasibility of direct communication and control with several distinct brain signals. But useful BCI applications that can benefit more than a small number of individuals await improvements in the speed and accuracy of this technology. It is widely assumed that BCI technology is simply a matter of decoding brain sig...

Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the...

Many people with severe motor disabilities lack the muscle control that would allow them to rely on conventional methods of augmentative communication and control. Numerous studies over the past two decades have indicated that scalp-recorded electroencephalographic (EEG) activity can be the basis for non-muscular communication and control systems,...

Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signal...

In the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or laten...

People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building...

A brain–computer interface (BCI) uses signals recorded from the brain to convey the user's intent. BCIs can be used for communication or can provide control signals for robotic and prosthetic devices. In studies to date, both invasive and noninvasive recording methods have proved effective and have reached comparable levels of performance. The majo...

Auditory perceptual and visual-spatial characteristics of subjective tinnitus evoked by eye gaze were studied in two adult human subjects. This uncommon form of tinnitus occurred approximately 4-6 weeks following neurosurgery for gross total excision of space-occupying lesions of the cerebellopontine angle and hearing was lost in the operated ear....

Our experiments were aimed at determining if the delayed matching-to-sample paradigm (DMS) could be used to study short-term acoustic recognition memory in young children and whether or not differences exist between children and adults. Our results indicate that the DMS paradigm can produce reliable data in young children. The decay of acoustic inf...

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 highlight...

Brain activity produces electrical signals detectable on the scalp or cortical surface or within the brain. BCIs translate these signals from mere reflections of brain activity into outputs that communicate the user's intent without the participation of peripheral nerves and muscles. Because they don't depend on neuromuscular control, BCIs can prov...

Neuroplasticity involved in acquiring a new cognitive skill was investigated with standard time domain event-related potentials (ERPs) of scalp-recorded electroencephalographic (EEG) activity and frequency domain analysis of EEG oscillations looking at the event-related synchronization (ERS) and desynchronization (ERD) of neural activity. Electroen...

People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoreg...

Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasi...

Correction of sensory transmission delays is an intractable problem because there is no absolute reference for calibration. Phase alignment is a practical alternative solution and can be realized by adaptive filters that operate locally with simple error signals.

Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient...

This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise...

Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibilit...

Brain-computer interface (BCI) research deals with establishing communication pathways between the brain and external devices where such pathways do not otherwise exist. Throughout the world, such research is surprisingly extensive and expanding. BCI research is rapidly approaching a level of first-generation medical practice for use by individuals...

Conditions such as amyotrophic lateral sclerosis (ALS), brainstem stroke, and severe brain or spinal cord injury can impair the neural pathways that control muscles or impair the muscles themselves. Individuals most severely affected may lose all voluntary muscle control, including eye movements and respiration, and may be completely locked in to t...

Due to the large differences in the biophysical characteristics between multi-microelectrode array and EEG/ECoG recordings, this report treats them separately. The signal processing modalities required to spatially resolve, extract features, and interpret intent depend on the selection of recording modality, which includes electroencephalography (E...

The impetus behind research into the establishment of communications pathways between the brain and external devices, or brain-computer interfaces (BCI), can be traced back to studies conducted in the 1970s postulating algorithms that correlated the firing patterns of motor cortex neurons with specific muscular responses. In the intervening decades...

Brain-computer interface (BCI) research deals with establishing communication pathways between the brain and external devices. BCI systems can be broadly classified depending on the placement of the electrodes used to detect and measure neurons firing in the brain: in invasive systems, electrodes are inserted directly into the cortex; in noninvasiv...

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) ov...

In this paper, we describe and evaluate the performance of a linear classifier learning technique for use in a brain-computer interface. Electroencephalogram (EEG) signals acquired from individual subjets are analyzed with this technique in order to detect responses to visual stimuli. Signal processing and classification are used for implementing a...

The latest research in the development of technologies that will allow humans to communicate, using brain signals only, with computers, wheelchairs, prostheses, and other devices. Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface...

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 (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepw...

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...

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial...

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities,...

Dans cette communication, nous décrivons et évaluons les performances d'une technique d'apprentissage des coefficients d'un classifieur linéaire utilisé dans une interface cerveau-ordinateur. Les signaux de l'électroencéphalogramme d'un individu sont analysés au moyen de cette technique afin de mettre en évidence les réponses de ce dernier à des st...

Autoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not n...

The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach an...

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...

This article argues for the use of modality specificity as a unifying framework by which to conceptualize and diagnose central auditory processing disorder (CAPD). The intent is to generate dialogue and critical discussion in this area of study. Research in the cognitive, behavioral, and neural sciences that relates to the concept of modality speci...