Evidence map›Paper›PMID 25599079›Full record

ArticleBrain computer interfaces (Abingdon, England)2014

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface.

Tim M Blakely, Jared D Olson, Kai J Miller, Rajesh P N Rao, Jeffrey G Ojemann

Abstract read
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In one paragraph

Article in Brain computer interfaces (Abingdon, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tim M BlakelyDepartment of Bioengineering, University of Washington, Seattle WA, 2419 8th Ave N Apt 402, Seattle, WA 98109. This author's research studies the underlying cortical organization of human sensorimotor function, specializing in multi-day micro-electrocorticographic array recordings in human subjects. This type of research is applicable in many areas of cognitive neuroscience, from brain-computer interfacing to neural engineering and robotics.
Jared D OlsonDepartment of Rehabilitation Medicine, University of Washington, Seattle, WA. Center for Sensorimotor Neural Engineering, Seattle, WA, Box 359740, 325 9 Avenue, Seattle, WA 98104, 206-744-5862, The author researches human sensorimotor neurophysiology, clinical translation of brain-machine interfaces, and is a physiatrist (physical medicine and rehabilitation physician) specializing in neurorehabilitation.
Kai J MillerPrior Affiliation: Department of Neurobiology and Behavior, University of Washington, Seattle, WA. Current Affiliation: Department of Neurological Surgery, Stanford School of Medicine, Stanford, CA, 300 Pasteur Drive, Stanford, CA 94305-5327, Kai is interested in distributed brain dynamics and systems neuroscience, and is currently in a neurosurgery residency.
Rajesh P N RaoCenter for Sensorimotor Neural Engineering, Seattle, WA. Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, (206) 685-9141. The author's research spans the areas of brain-computer interfacing, computational neuroscience and robotics.
Jeffrey G OjemannNeurological Surgery and Radiology, University of Washington, Center for Sensorimotor Neural Engineering, Seattle, WA, Center for Integrative Brain Research, Seattle Children's Research Institute, Box 359300, Seattle, WA 98195, (206) 987-4240. This author's research is electrocorticographic studies of cognitive function and brain-machine interface and is a neurosurgeon specializing in the treatment of pediatric and epilepsy neurosurgery.

Funding

Rehabilitation Medicine Scientist Training (RMST) ProgramK12HD001097 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BONINGER, MICHAEL L. · 1995 to 2016
$14.1M
CLINICAL NEUROSCIENCES TRAINING PROGRAMT32NS007144 · NINDS · UNIVERSITY OF WASHINGTON · PI OJEMANN, GEORGE A · 1985 to 2008
$2.0M
Electrocorticography signals for human hand prostheticsR01NS065186 · NINDS · UNIVERSITY OF WASHINGTON · PI OJEMANN, JEFFREY G · 2010 to 2014
$1.6M
NICHD NIH HHS K12 HD001097NINDS NIH HHS R01 NS065186NINDS NIH HHS T32 NS007144
6 · The paper itself

Abstract

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75-200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms.

Indexed as

Brain-Computer Interface (BCI)Brain-Machine Interface (BMI)electrocorticography (ECoG)high gammamotor imagerymotor learning

Identifiers

PMID25599079
PMCPMC4295512

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.