Towards zero training for brain-computer interfacing
August 13, 2008While invasive electrode recordings in humans show long-term promise, non-invasive techniques can also provide effective brain-computer interfacing (BCI) and localization of motor activity in the brain for paralyzed patients with significantly reduced risks and costs as well as novel applications for healthy users. However, two issues hamper the ease of use of BCI systems based on non-invasive recording techniques, such as electroencephalography (EEG).
First, the demands for electrode preparation for multi-channel EEG – necessary for optimal performance – are significant. Second, EEG signals are highly subject-specific and vary considerably even between recording sessions of the same user performing the same experimental paradigm.
Therefore, the BCI software that includes preprocessing and classification needed to be adapted individually for optimal performance before every session. While Popescu et al. (Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes, PLoS ONE, 2007) have proposed a solution to the first issue by introducing dry electrodes, which can reduce the EEG electrode preparation time from 40 minutes to one minute, the second problem has, until now, remained unsolved.
Reporting in the online, open-access journal PLoS ONE, on August 13, a new study by Matthias Krauledat and colleagues at the Berlin Institute of Technology suggests a novel data analysis method that bypasses the need for the time-consuming calibration for long-term BCI users and may reduce the calibration time from 20?? minutes to one minute. This is achieved by a clustering approach, which extracts most representative spatial filters for each individual subject from prior recordings.
Taken together, these developments of the Berlin BCI group pave the way to make BCI technology more practical for daily use in man-machine interaction both for patients and for the healthy.
Krauledat M, Tangermann M, Blankertz B, Müller K-R (2008) Towards Zero Training for Brain-Computer Interfacing. PLoS ONE 3(8): e2967. doi:10.1371/journal.pone.0002967 http://dx.plos.org/10.1371/journal.pone.0002967
Source: Public Library of Science
Oct 06, 2009 |
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I do wish to address a misleading facet of this article. It says that BCIs require extensive training, and that the Berlin group has solved this previously "unsolved" challenge. This is not accurate for all BCIs - only the movement based approach that their group uses. Many BCIs rely on evoked potentials (such as the P300 or SSVEP) and require no training at all. Other BCIs rely on imagined movement (such as the Wolpaw BCI approach), but different tasks that would probably not benefit much from the Berlin lab's approach.
I strongly agree that reducing dependence on outside experts is a top priority in BCI research. This is the central theme of the grant that I wrote and now manage, called BRAIN (BCIs with Rapid Automated Interfaces for Nonexperts). Improved sensors and reduced dependence on expert help to train users and/or the BCI are both very important in this grant, as is developing easy access to different applications. We hope that these efforts, along with efforts from the Berlin group and others, will help make BCIs into practical communication tools for laypeople who cannot communicate via conventional means due to disability.
-- Brendan Allison, PhD