Parallel man-machine training in development of EEG-based cursor control

Kostov, A.; Polak, Mark J.

doi:10.1109/86.847816

Cited by 153 publications

(64 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…4 compares the 1994 study (24) and the present study in terms of their correlations (measured as R 2 ) between the vertical and horizontal variables and the vertical and horizontal dimensions of target location. [The very brief description of a second early study by Kostov and Pollock (31) does not give sufficient information for inclusion in this comparison. It appears to describe a low level of control like that of our 1994 study (24).]…”

Section: Discussionmentioning

confidence: 99%

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Wolpaw

McFarland

2004

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

1,299

954

View full text Add to dashboard Cite

Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show 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. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.brain-machine interface ͉ electroencephalography

show abstract

Section: Discussionmentioning

confidence: 99%

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Wolpaw

McFarland

2004

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

1,299

954

View full text Add to dashboard Cite

show abstract

“…Most subsequent studies of human cortical electrical behavior have focused on the spectral characteristics of EEG. The EEG frequency band ranges that have classically been associated with motor output are the 8 -12 Hz (""), 18 -26 Hz ("␤"), and everything Ͼ30 Hz ("␥") (Wolpaw et al, 1991;Kostov and Polak, 2000;Pfurtscheller, 2000). The lower bands have been associated with thalamocortical circuits and typically decrease in amplitude in association with actual or imagined movements (Levine et al, 1999;Rohde et al, 2002;Pfurtscheller et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Spectral Changes in Cortical Surface Potentials during Motor Movement

et al. 2007

View full text Add to dashboard Cite

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76 -100 Hz) and a low-frequency band (LFB) (8 -32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.

show abstract

“…The experimental approach was developed based on current understanding of sensorimotor rhythms and on the methodology of current EEG-based BCIs that use these rhythms [18] (see [1] for review). Sensorimotor rhythms comprise µ (8-12 Hz), β (18-26 Hz) and γ (>30 Hz) oscillations [6,[19][20][21][22]. They are thought to be produced by thalamocortical circuits and they change in amplitude in association with actual or imagined movements [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A brain–computer interface using electrocorticographic signals in humans

et al. 2004

View full text Add to dashboard Cite

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain. M This article features online multimedia enhancements

show abstract

Parallel man-machine training in development of EEG-based cursor control

Cited by 153 publications

References 6 publications

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Spectral Changes in Cortical Surface Potentials during Motor Movement

A brain–computer interface using electrocorticographic signals in humans

Contact Info

Product

Resources

About