• Corpus ID: 17893107

Pushing the Communication Speed Limit of a Noninvasive BCI Speller

  title={Pushing the Communication Speed Limit of a Noninvasive BCI Speller},
  author={Po T. Wang and Christine E. King and An H. Do and Zoran Nenadic},
Electroencephalogram (EEG) based brain-computer interfaces (BCI) may provide a means of communication for those affected by severe paralysis. However, the relatively low information transfer rates (ITR) of these systems, currently limited to 1 bit/sec, present a serious obstacle to their widespread adoption in both clinical and non-clinical applications. Here, we report on the development of a novel noninvasive BCI communication system that achieves ITRs that are severalfold higher than those… 

Figures and Tables from this paper

Evaluating True BCI Communication Rate through Mutual Information and Language Models
A mutual information-based metric that incorporates prior information and a model of systematic errors is presented that is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner.
EMG speller with adaptive stimulus rate and dictionary support
This paper analyzes requirements for developing interfaces for disabled users and interfaces of known speller applications, and describes the development of the EMG-based speller as a benchmark application, and evaluates performance and usability of the developed speller.
Development of a concept-based EMG-based speller
This work analyzes and develops an EMG-based speller application with a traditional letter-based as well as visual concept-based interface and evaluates the performance and usability of the developed speller using empirical metrics.
The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia
This proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible and if this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified.
Localization of Brain Activity in Electroencephalography Data during Brain-Computer Interface Operation
A novel subspace-based technique was used to suppress spatially correlated EEG interference sources, followed by a technique that estimates the source parameters with a near maximum likelihood performance, which found sources to correlate with event-related potentials (ERPs) and are thus hypothesized to be responsible for the N200 and P300 ERPs.
Neural Signals Evoked by Stimuli of Increasing Social Scene Complexity Are Detectable at the Single-Trial Level and Right Lateralized
complex ecological animations with social content elicit neurophysiological events which can be characterized even at the single-trial level, right lateralized, which suggests the feasibility of brain computer interfaces that can be applied to social cognition disorders such as autism.
Brain-computer interfaces for virtual Quadcopters based on a spiking-neural network architecture - NeuCube
This chapter discusses the motivation, structure, and structure of the thesis, as well as the research scope and focus, and the mechanism of the brain function and data collection in this study.
Toward real-time communication using brain-computer interface systems
Appendix: the Feasibility of a Brain-computer Interface Functional Electrical Stimulation System for the Restoration of Overground Walking after Paraplegia 1 Methods 1.1 Inclusion/exclusion Criteria 1.2 Screening and Imaging


A high-performance brain–computer interface
The design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported are presented, indicating that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible.
An improved P300-based brain-computer interface
The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.
Brain-computer interface technology: a review of the first international meeting.
  • J. Wolpaw, N. Birbaumer, T. Vaughan
  • Computer Science
    IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
  • 2000
The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.
A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns
Brain–computer interfaces for communication and control
Evaluation of P300-Based Brain-Computer Interface in Real-World Contexts
Assessing how background noise and interface color contrast affect user performance and BCI usage preference in a P300-based BCI system should give some insight to the real-world applicability of the current P300 Speller as a nonmuscular communication system, especially for individuals with severe neuromuscular disabilities.
EEG-based neuroprosthesis control: A step towards clinical practice
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.
  • J. Wolpaw, D. McFarland
  • Biology, Medicine
    Proceedings of the National Academy of Sciences of the United States of America
  • 2004
It is shown that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys.
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.
Electroencephalographic (EEG) control of three-dimensional movement.
This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control, and suggests that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.