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The self-paced graz brain-computer interface: methods and applications.

Scherer R, Schloegl A, Lee F, Bischof H, Jansa J, Pfurtscheller G - Comput Intell Neurosci (2007)

Bottom Line: The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels.Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface.The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria. reinhold.scherer@tugraz.at

ABSTRACT
We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

No MeSH data available.


(a) Map of the freeSpace virtual environment showingthe best performance (covered distance) for each subject. (b) Frequencies ofoccurrence of the detected motor-imagery tasks (selected navigation commands).
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fig6: (a) Map of the freeSpace virtual environment showingthe best performance (covered distance) for each subject. (b) Frequencies ofoccurrence of the detected motor-imagery tasks (selected navigation commands).

Mentions: The percentageof EEG samples classified as EMG artifact during the training procedure wasless than 0.9% for each subject. Figure 5(a) shows example EMG detections. Themethod works well for minor (upper plot) as well as for massive muscle activity(lower plot). The power density spectrum for each channel and motor imagerytask is summarized in Figure 6. The power density spectrum was computed by averagingthe power spectrum of the forty motor imagery trials for each class recordedduring the last cue-based feedback session. Before computing the discreteFourier transform of the 4-second motor imagery segment (see Figure 2(b)) ahamming window was applied. The spectra show clear peaks in the upper-alpha(10–12 Hz) and upper-beta bands (20–25 Hz).


The self-paced graz brain-computer interface: methods and applications.

Scherer R, Schloegl A, Lee F, Bischof H, Jansa J, Pfurtscheller G - Comput Intell Neurosci (2007)

(a) Map of the freeSpace virtual environment showingthe best performance (covered distance) for each subject. (b) Frequencies ofoccurrence of the detected motor-imagery tasks (selected navigation commands).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266812&req=5

fig6: (a) Map of the freeSpace virtual environment showingthe best performance (covered distance) for each subject. (b) Frequencies ofoccurrence of the detected motor-imagery tasks (selected navigation commands).
Mentions: The percentageof EEG samples classified as EMG artifact during the training procedure wasless than 0.9% for each subject. Figure 5(a) shows example EMG detections. Themethod works well for minor (upper plot) as well as for massive muscle activity(lower plot). The power density spectrum for each channel and motor imagerytask is summarized in Figure 6. The power density spectrum was computed by averagingthe power spectrum of the forty motor imagery trials for each class recordedduring the last cue-based feedback session. Before computing the discreteFourier transform of the 4-second motor imagery segment (see Figure 2(b)) ahamming window was applied. The spectra show clear peaks in the upper-alpha(10–12 Hz) and upper-beta bands (20–25 Hz).

Bottom Line: The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels.Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface.The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria. reinhold.scherer@tugraz.at

ABSTRACT
We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

No MeSH data available.