Language Model Applications to Spelling with Brain-Computer Interfaces

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models appli

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

A Novel Stimulation Paradigm For a Brain-computer Interface Based On Ssvep

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Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time bject detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimuli - as both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. On-screen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects.Comment: Accepted as a full paper at the 2019 International Conference on Robotics and Automation (ICRA

State-dependent modulation of cortico-spinal networks

Beta-band rhythm (13-30 Hz) is a dominant oscillatory activity in the sensorimotor system. Numerous studies reported on links between motor performance and the cortical and cortico-spinal beta rhythm. However, these studies report divergent beta-band frequencies and are, additionally, based on differently performed motor-tasks (e.g., motor imagination, muscle contraction, reach, grasp, and attention). This diversity blurs the role of beta in the sensorimotor system. It consequently challenges the development of beta-band activity-dependent stimulation protocols in the sensorimotor system. In this vein, we studied the functional role of beta-band cortico-cortical and cortico-spinal networks during a motor learning task. We studied how the contribution of cortical and spinal beta changes in the course of learning, and how this modulation is affected by afferent feedback to the sensorimotor system. We furthermore researched the relationship to motor performance. Consider that we made our study in the absence of any residual movement to allow our findings to be translated into rehabilitation programs for severely affected stroke patients. This thesis, at first, investigates evoked responses after transcranial magnetic stimulation (TMS). This revealed two different beta-band networks, i.e., in the low and high beta-band reflecting cortical and cortico-spinal activity. We, then, used a broader frequency range in the beta-band to trigger passive opening of the hand (peripheral feedback) or cortical stimulation (cortical feedback). While a unilateral hemispheric increase in cortico-spinal synchronization was observed in the group with peripheral feedback, a bilateral hemispheric increase in cortico-cortical and cortico-spinal synchronization was observed for the group with cortical feedback. An improvement in motor performance was found in the peripheral group only. Additionally, an enhancement in the directed cortico-spinal synchronization from cortex to periphery was observed for the peripheral group. Similar neurophysiological and behavioral changes were observed for stroke patients receiving peripheral feedback. The results 6 suggest two different mechanisms for beta-band activity-dependent protocols depending on the feedback modality. While the peripheral feedback appears to increase the synchronization among neural groups, cortical stimulation appears to recruit dormant neurons and to extend the involved motor network. These findings may provide insights regarding the mechanism behind novel activity-dependent protocols. It also highlights the importance of afferent feedback for motor restoration in beta-band activity-dependent rehabilitation programs

Southwest Research Institute assistance to NASA in biomedical areas of the technology

Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Some effects of chronic monocular paralysis in adult cats rapidly reversed by paralyzing the second eye

Disruptions in binocular stimulation induced by two or more weeks of monocular paralysis (ChMP), reduce the encounter rates for Xrelative to Y-type LGN cells (a reduction in the X/Y ratio) during semichronic recordings from adult cats. Less than four days of monocular paralysis (AcMP) has no impact upon the X/Y ratio. The processes underlying ChMP'.s impact upon the X/Y ratio are not passive or degenerative, and are therefore active processes such as 1) excitability changes in X-cells, in Y-cells, or in both cell types; or 2) change in the functional characteristics of X-cells such that they are systematically reclassified as Y-cells. These alternatives were explored using standard extracellular unit recording procedures, including classification of cells as X- or Y-type with a common battery of receptive field and physiological measures. Preliminary experiments indicated that nitrous oxide anesthesia reverses the impact of ChMP, rendering the X/Y ratio of ChMP equivalent to that of AcMP (which is unaffected by anesthesia), and that this effect is so robust that it is evident in single electrode penetrations