A brain–computer interface using electrocorticographic signals in humans

Leuthardt, Eric C.; Schalk, Gerwin; Wolpaw, Jonathan R.; Ojemann, Jeffrey G.; Moran, Daniel W.

doi:10.1088/1741-2560/1/2/001

Cited by 1,044 publications

(781 citation statements)

References 47 publications

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“…In contrast, power in the high-gamma (70-100 Hz) band increases very locally in response to specific motor movement or imagery (22). Both signals have been successfully used for BCI control (23)(24)(25). During each task trial, a cursor was presented vertically centered on the far left side of the screen and moved at a fixed rate across the screen to the right side.…”

Section: Resultsmentioning

confidence: 99%

Sleep spindles are locally modulated by training on a brain–computer interface

Johnson

Blakely

Hermes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motorbased brain-computer interface device.

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Section: Resultsmentioning

confidence: 99%

Sleep spindles are locally modulated by training on a brain–computer interface

Johnson

Blakely

Hermes

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Indeed, the extended frequency response of invasive EEG recordings has shown that during functional activation of cerebral cortex, gamma activity occurs over a much broader range of frequencies that extend far beyond the traditional 40-Hz gamma band typically observed in scalp EEG (Crone et al, 1998a;Crone et al, 2006). These non-phase-locked responses in "high-gamma" frequencies, typically greater than 60 Hz and extending as high as 200 Hz, have been observed during functional activation in a variety of cortical domains, including sensorimotor (Crone et al, 1998a;Ohara et al, 2000;Pfurtscheller et al, 2003;Leuthardt et al, 2004), oculomotor (Lachaux et. al., 2006), auditory (Crone et al, 2001a;Ray et al, 2003;Edwards et al, 2005), visual (Crone et al, 2001b;Lachaux et al, 2005;Tanji et al, 2005), and language (Crone et al, 2001b;Sinai et al, 2005) cortices.…”

Section: Introductionmentioning

confidence: 99%

High-frequency gamma activity (80–150Hz) is increased in human cortex during selective attention

Ray

Niebur

Hsiao

et al. 2008

Clinical Neurophysiology

215

163

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Objective: To study the role of gamma oscillations (>30 Hz) in selective attention using subdural electrocorticography (ECoG) in humans. Methods:We recorded ECoG in human subjects implanted with subdural electrodes for epilepsy surgery. Sequences of auditory tones and tactile vibrations of 800 ms duration were presented asynchronously, and subjects were asked to selectively attend to one of the two stimulus modalities in order to detect an amplitude increase at 400 ms in some of the stimuli.Results: Event-related ECoG gamma activity was greater over auditory cortex when subjects attended auditory stimuli and was greater over somatosensory cortex when subjects attended vibrotactile stimuli. Furthermore, gamma activity was also observed over prefrontal cortex when stimuli appeared in either modality, but only when they were attended. Attentional modulation of gamma power began ∼400 ms after stimulus onset, consistent with the temporal demands on attention. The increase in gamma activity was greatest at frequencies between 80 and 150 Hz, in the so-called high gamma frequency range.Conclusions: There appears to be a strong link between activity in the high-gamma range (80-150 Hz) and selective attention.Significance: Selective attention is correlated with increased activity in a frequency range that is significantly higher than what has been reported previously using EEG recordings.

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“…ECoG-based BCIs have controlled 1-or 2-dimensional cursor movements using motor or sensory imagery or working memory (dorsolateral prefrontal cortex). [87][88][89][90][91] An ECoG-based BCI can enable users to control a prosthetic hand or to select characters using motor-imagery or the P300 event-related potential. 12,[92][93][94] Most recently, ECoG signals measured over speech cortex during overt or imag-ined phoneme and word articulation were used for online cursor control 95 and were also accurately decoded off-line for potential application to direct speech synthesis.…”

Section: Bcis That Use Ecog Activitymentioning

confidence: 99%

Brain-Computer Interfaces in Medicine

Shih

Krusienski

Wolpaw

2012

Mayo Clinic Proceedings

562

279

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Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. U ntil recently, the dream of being able to control one's environment through thoughts had been in the realm of science fiction. However, the advance of technology has brought a new reality: Today, humans can use the electrical signals from brain activity to interact with, influence, or change their environments. The emerging field of brain-computer interface (BCI) technology may allow individuals unable to speak and/or use their limbs to once again communicate or operate assistive devices for walking and manipulating objects. Brain-computer interface research is an area of high public awareness. Videos on YouTube as well as news reports in the lay media indicate intense curiosity and interest in a field that hopefully one day soon will dramatically improve the lives of many disabled persons affected by a number of different disease processes.This review seeks to provide the general medical community with an introduction to BCIs. We define BCI and then review some of the seminal discoveries in this rapidly emerging field, the brain signals used by BCIs, the essential components of a BCI system, current BCI systems, and the key issues now engaging researchers. Challenges are inherent in translating any new technology to practical and useful clinical applications, and BCIs are no exception. We discuss the potent...

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A brain–computer interface using electrocorticographic signals in humans

Cited by 1,044 publications

References 47 publications

Sleep spindles are locally modulated by training on a brain–computer interface

Sleep spindles are locally modulated by training on a brain–computer interface

High-frequency gamma activity (80–150Hz) is increased in human cortex during selective attention

Brain-Computer Interfaces in Medicine

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