[Year 12 IT Apps] NASA brain-computer interfaces

[Stephen Loosley]

Hi all,

The NASA Intelligent Systems Division (http://ti.arc.nasa.gov/)
are sponsoring some interesting research.  One recent report
depicts two 'brain-computer interfaces' called 'Target Practice'
(of course) and 'Think Pointer'. Both apparently work quite well:

"Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials."

Trejo LJ,  Rosipal R,  and Matthews B.  NASA Ames Research Center, Moffett Field, CA 94035, USA. ltrejo at quasarusa.com 
Published: IEEE Trans Neural Syst Rehabil Eng. June 2006.

We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. 

Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display.  Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions..

The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point.. 

Training of the classifier requires about 3 min.. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning.   We have also developed a realistic demonstration of our system for control of a moving map display.  
(PMID: 16792300)
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Cheers all ..
Stephen Loosley
Melbourne, Australia
.