Real-time classification of activated brain areas for fMRI-based human-brain-interfaces

Moench, Tobias; Hollmann, Maurice; Grzeschik, Ramona; Mueller, Charles M.; Luetzkendorf, Ralf; Baecke, Sebastian; Luchtmann, Michael; Wagegg, Daniela; Bernarding, Johannes

doi:10.1117/12.769848

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data acquired by fMRI techniques were processed offline and the results only became available after several hours or even days [ 87 ]. fMRI-based BCIs have been made possible, thanks to the development of real-time fMRI [ 84 , 88 , 89 ]. The information transfer rate in fMRI-based BCIs is between 0.60 and 1.20 bits/min [ 90 ].…”

Section: Neuroimaging Approaches In Bcismentioning

confidence: 99%

Brain Computer Interfaces, a Review

Nicolás-Alonso

Gómez-Gil

2012

Sensors

1,704

962

View full text Add to dashboard Cite

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

show abstract

Section: Neuroimaging Approaches In Bcismentioning

confidence: 99%

Brain Computer Interfaces, a Review

Nicolás-Alonso

Gómez-Gil

2012

Sensors

1,704

962

View full text Add to dashboard Cite

show abstract

“…Thus virtual reality paradigms could be useful for hyperscanning. Preliminary reports of hyperscanning employing virtual reality include a study in which participants competed to increase regional brain activations [74].…”

Section: Social Cognitionmentioning

confidence: 99%

Applications of Virtual Reality Technology in Brain Imaging Studies

Chou¹,

Weingarten²,

Madden³

et al. 2012

Virtual Reality in Psychological, Medical and Pedagogical Applications

View full text Add to dashboard Cite

“…A much stronger, broadbanded and more localized signal can be recorded by using invasive methods, either placing electrodes on the cortical surface with electrocorticography (ECoG) [12] or placing microelectrodes inside the cortex [8]. A number of studies have also been made where the control signal is based on real-time fMRI [4], [13], [14], [16], [17]. Using fMRI one has access to the whole brain with a high resolution.…”

Section: Introductionmentioning

confidence: 99%

fMRI based BCI control using spatial visual attention at 7T

Andersson

Viergever

Pluim

et al. 2009

2009 4th International IEEE/EMBS Conference on Neural Engineering

View full text Add to dashboard Cite

In this study we have investigated the potential of using visual attention as a control-method for Brain-Computer-Interfaces (BCI). We show using high-field (7T) fMRI that subjects can control their visuospatial attention and that the BOLD effect is strong enough to base a reliable control-signal on. The strength and location of activation was determined for six subjects. In an additional experiment two of the subjects got real-time feedback based on the activation in the activated regions.

show abstract

Real-time classification of activated brain areas for fMRI-based human-brain-interfaces

Cited by 5 publications

References 0 publications

Brain Computer Interfaces, a Review

Brain Computer Interfaces, a Review

Applications of Virtual Reality Technology in Brain Imaging Studies

fMRI based BCI control using spatial visual attention at 7T

Contact Info

Product

Resources

About