Cortical Imaging of Sensorimotor Rhythm during On-line Control of Brain-computer Interface

Yuan, Han; Doud, Alexander; Gururajan, A.; He, Bin

doi:10.1109/nfsi-icfbi.2007.4387766

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…PC was chosen purposefully to facilitate comparison to the previously published work that allowed the trial to time out (e.g. Wolpaw and McFarland 2004, Yuan et al 2007, Yuan et al 2008). PCNA is more relevant to non-computer applications, such as controlling a wheelchair or a neuroprosthetic, where an abort might entail the wheelchair or the prosthetic returning to its starting position.…”

Section: Discussionmentioning

confidence: 99%

“…The next experimental paradigm was process control with no aborts (PCNA), illustrated in figure 1(D). PCNA was a typical cursor task used in BCI studies (Wolpaw and McFarland 2004, Krusienski et al 2007, Yuan et al 2007, Blankertz et al 2008, where the user controlled the movement of the cursor to hit a target. Similar to GSFD, PCNA was intended to not allow the subjects to abort a trial through timing out.…”

Section: Experimental Paradigmsmentioning

confidence: 99%

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Goal selection versus process control in a brain–computer interface based on sensorimotor rhythms

Royer

2009

J. Neural Eng.

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In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for 7/8 subjects), and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Experimental Paradigmsmentioning

confidence: 99%

Goal selection versus process control in a brain–computer interface based on sensorimotor rhythms

Royer

2009

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…PC was chosen purposefully to facilitate comparison to the previously published work that allowed the trial to time out (e.g. Wolpaw and McFarland 2004, Yuan et al 2007. PCNA is more relevant to non-computer applications, such as controlling a wheelchair or a neuroprosthetic, where an abort might entail the wheelchair or the prosthetic returning to its starting position.…”

Section: Discussionmentioning

confidence: 99%

Boundary values of functions in a Sobolev space with Muckenhoupt weight on some non-Lipschitz domains

Tyulenev¹

2014

Sb. Math.

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In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

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“…This inherently requires the use of functional neuroimaging techniques to identify the MI-related patterns and to employ them in a neurofeedback protocol. In electroencephalography (EEG)-based applications, the so-called sensorimotor rhythms (SMRs; encompassing the µ and β frequency bands) are usually targeted, since performing MI of a limb produces event-related desynchronizations (ERDs) over the contralateral primary sensorimotor cortex and event-related synchronizations (ERSs) over the same area, but ipsilaterally (Pfurtscheller et al 1997, Pfurtscheller and Lopes Da Silva 1999and Neuper et al 2005, Yuan et al 2007. Thus, by feeding back the SMR brain activity in real-time, subjects could be able to learn to modulate it and, hence, profit from the benefits of an MI-based neuromodulation rehabilitation protocol.…”

Section: Introductionmentioning

confidence: 99%

Motor imagery practice and feedback effects on functional connectivity

Filho¹,

Attux²,

Castellano³

2021

J. Neural Eng.

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– Objective: the use of motor imagery (MI) in motor rehabilitation protocols has been increasingly investigated as a potential technique for enhancing traditional treatments, yielding better clinical outcomes. However, since MI performance can be challenging, practice is usually required. This demands appropriate training, actively engaging the MI-related brain areas, consequently enabling the user to properly benefit from it. The role of feedback is central for MI practice. Yet, assessing which underlying neural changes are feedback-specific or purely due to MI practice is still a challenging effort, mainly due to the difficulty in isolating their contributions. In this work, we aimed to assess functional connectivity (FC) changes following MI practice that are either extrinsic or specific to feedback. Approach: to achieve this, we investigated FC, using graph theory, in electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, during MI performance and at resting-state (rs), respectively. Thirty healthy subjects were divided into three groups, receiving no feedback (control), “false” feedback (sham) or actual neurofeedback (active). Participants underwent 12 to 13 hands-MI EEG sessions and pre- and post-MI training fMRI exams. Main results: following MI practice, control participants presented significant increases in degree and in eigenvector centrality for occipital nodes at rs-fMRI scans, whereas sham-feedback produced similar effects, but to a lesser extent. Therefore, MI practice, by itself, seems to stimulate visual information processing mechanisms that become apparent during basal brain activity. Additionally, only the active group displayed decreases in inter-subject FC patterns, both during MI performance and at rs-fMRI. Significance: hence, actual neurofeedback impacted FC by disrupting common inter-subject patterns, suggesting that subject-specific neural plasticity mechanisms become important. Future studies should consider this when designing experimental NFBT protocols and analyses.

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Cortical Imaging of Sensorimotor Rhythm during On-line Control of Brain-computer Interface

Cited by 4 publications

References 13 publications

Goal selection versus process control in a brain–computer interface based on sensorimotor rhythms

Goal selection versus process control in a brain–computer interface based on sensorimotor rhythms

Boundary values of functions in a Sobolev space with Muckenhoupt weight on some non-Lipschitz domains

Motor imagery practice and feedback effects on functional connectivity

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