ECoG factors underlying multimodal control of a brain-computer interface

Wilson, Jeffrey A.; Felton, Elizabeth A.; Garell, P. Charles; Schalk, Gerwin; Williams, Justin

doi:10.1109/tnsre.2006.875570

Cited by 146 publications

(115 citation statements)

References 17 publications

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“…These human results have since been replicated and further extended for decoding of arm movements (Pistohl et al, 2008;Sanchez et al, 2008), flexion of individual fingers (Kubanek et al, 2007), and even specific aspects (such as the type of phoneme) of actual or imagined speech (Schalk et al, 2007a). Finally, several studies demonstrated that it is possible to combine general understanding of motor-related ECoG responses (Crone et al, 1998a,b;Aoki et al, 1999;Graimann et al, 2002;Sinai et al, 2005;Leuthardt et al, 2007;Miller et al, 2007) with the more specific understanding described above to implement different ECoG-based BCIs (Leuthardt et al, 2004;Wilson et al, 2006;Felton et al, 2007;Schalk et al, 2008) in humans using the BCI2000 software framework . These studies showed that ECoG supports accurate nonmuscular one-or two-dimensional movement control in humans with little subject training.…”

Section: Signal Fidelity Of Ecogmentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

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258

176

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IntroductionTechnological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.

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Section: Signal Fidelity Of Ecogmentioning

confidence: 99%

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Kipke¹,

Shain²,

Buzsáki³

et al. 2008

J. Neurosci.

Self Cite

258

176

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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

542

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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“…The AI was then determined for each electrode by the feature with the largest normalized change between rest and movement states for all hand movements: (3) The five electrodes with the highest AI values were considered to represent hand movementrelated locations for the purposes of connectivity analysis. Five electrodes were chosen because for all subjects except Subject C, previous work has shown that within five electrodes, decoding accuracy (r, correlation between actual and predicted hand movements) reached approximately 95% of maximum decoding accuracy [12].…”

Section: Ecog Electrode Activation Indexmentioning

confidence: 99%

Connectivity analysis as a novel approach to motor decoding for prosthesis control

Benz

Zhang

Bezerianos

et al. 2011

IEEE Trans. Biomed. Eng.

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The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r 2 ) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.

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ECoG factors underlying multimodal control of a brain-computer interface

Cited by 146 publications

References 17 publications

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

Brain-Computer Interfaces in Medicine

Connectivity analysis as a novel approach to motor decoding for prosthesis control

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