BibTex format
@inproceedings{Abbott:2013,
author = {Abbott, WW and Faisal, AA},
title = {Large field study of ultra-low cost BMI using intention decoding from eye movements for closed loop control.},
year = {2013}
}
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TY - CPAPER
AB - Developments in the field of brain machine interfaces (BMI) hold the hope to restore independence to patients with severe motor disabilities via neuroprosthetics. However, current technology is either highly invasive or suffers from long training times, low information transfer rates, high latencies and high clinical costs (Tonet et al., 2008, J. Neurosci. Methods). We propose a non-invasive and ultra-low cost alternative - intention decoding from 3D gaze signals (Abbott & Faisal, 2012, J. Neural Eng.). Eye movements provide a high frequency signal directly relevant for neuroprosthetic control and are retained by patients with serious motor deficiencies, paralysis and limb amputation (Kaminski et al., 2002, Ann. N. Y. Acad. Sci.; Kaminski et al. 1992, Ann. Neurol.). Our system allows read-out bit rates of 43 bit s-1, well beyond conventional BMIs (EEG 1.63 bit s-1, MEA 3.3 bit s-1, EMG 2.66 bit s-1 , making our task-level BMI suitable for closed loop control of neuroprosthetics (Tonet et al., 2008, J. Neurosci. Methods; Abbott & Faisal, 2012, J. Neural Eng.). In the present study we performed a large-scale field study (n=867) to determine if naïve subjects could use our BMI to compete in an arcade video game (Pong - computer tennis). Two arcade cabinets with our embedded BMI system were built that allowed members of the public to briefly calibrate and then to play a game of pong (first to 5 points), controlling the paddle using just their eyes. Their opponent was either an AI computer player or another human player using a conventional control pad (up-down) input. During game play, the eye position, gaze estimation, control position and game state were recorded. Following a 30 second calibration, games lasted 76±34 seconds and subjects successfully returned 6.5±6.2 shots against their opponent (mean ± standard deviation). The average score when the subjects lost was 0.7±1.1 compared to the opponent average losing score of 2.1
AU - Abbott,WW
AU - Faisal,AA
PY - 2013///
TI - Large field study of ultra-low cost BMI using intention decoding from eye movements for closed loop control.
ER -