[tt] Self-Paced Brain-Computer Interface

[Hughes, James J.]

More information: Fatourechi, M, Ward, R K, and Birch, G E. "A
self-paced brain-computer interface system with a low false positive
rate." J. Neural Eng. 5 (2008) 9-23.

http://www.physorg.com/printnews.php?newsid=119616130

Self-Paced Brain-Computer Interface Gets Closer to Reality

Data from a sample EEG is measured from one second before to one second
after a decision point. The data is used to classify the neurological
phenomena when making a decision. Credit: Fatourechi et al.
Data from a sample EEG is measured from one second before to one second
after a decision point. The data is used to classify the neurological
phenomena when making a decision. Credit: Fatourechi, et al.

Using the human mind to control computers could lead to a wide range of
applications, such as giving people with limited motion the ability to
operate machines. However, translating thoughts into actions is a great
challenge for researchers. How can a system determine which thoughts
should be acted upon, and which thoughts are merely personal thoughts
and therefore should be ignored by the system?

More importantly, asks Dr. Mehrdad Fatourechi, can the system provide
the users with the ability to control a computer whenever they want?
These are the questions that Fatourechi and other "self-paced" brain
computer interface (BCI) researchers are trying to answer.

So far, no self-paced BCI system has performed well enough to be
suitable for practical applications. But Fatourechi, along with
Professors Dr. Rabab K. Ward and Dr. Gary E. Birch from the University
of British Columbia, Canada, have recently made a significant
improvement with the development of a self-paced, fully automated
brain-computer interface. The group's results are published in a recent
issue of the Journal of Neural Engineering.

To test the abilities of a self-paced BCI, researchers often ask
volunteers to perform a specific mental activity, such as to attempt to
move their right index finger. The system then tries to detect the
changes in the brain signals related to this mental activity (called
neurological phenomenon) and map them into a control command for the
device.

To researchers, confronting this problem means striving for a low false
positive rate (when the system accidentally performs an action that the
user did not intend) combined with a high true positive rate (when the
system accurately identifies and acts upon a user's mental commands).
Researchers generally consider a true-positive rate of 70% to be
acceptable for realistic situations. The false positive rate should
ideally be zero, since a system that acts spontaneously would be very
frustrating to users.

"The main reason [that it's difficult to differentiate EEG signals] is
that the signal-to-noise ratio of the neurological phenomena that the
system tries to detect is very low," Fatourechi told PhysOrg.com. "In
other words, the level of noise is very high in a BCI system. Our system
uses a rather complex feature classification method to achieve this
task."

Previously, the researchers had developed a self-paced BCI that could
achieve a very low false positive rate (0.5%), but the true positive
rate was also quite low, at 27.3%. The interface used three separate
neurological phenomena: movement-related potentials, and changes in the
power of Beta and Mu rhythms. All of these neurological phenomena are
believed to be present in the EEG signals when a user attempts to
perform a movement activity, so the researchers postulated that their
simultaneous detection could improve the performance of the BCI systems.

This previous BCI system was considered to be semi-automated because
some parts of the design were not automatic. In an attempt to improve
the true-positive rate, the researchers changed the design to be fully
automated in a few ways. First, they developed a "hybrid genetic
algorithm" which automatically and simultaneously selects the parameters
of different parts of the system that together yield the near-optimal
performance. Also, the researchers employed more complex feature
extraction and classification methods, and instead of using monopolar
EEG signals, they used bipolar EEG signals to increase the number of the
signals. The bipolar signals could also generate more discriminant
features of different signals used for different thought processes.

"We believe the key factor is fully automating the structure of the
system," said Fatourechi. "Selecting a good 'model' for a BCI system is
a delicate process, and if it is not done properly, it can decrease the
performance significantly. By 'fully' automating the model selection
process, we removed any subjectivity from the design of our BCI system
and, as a result, improved the performance."

Incorporating these improvements, the researchers' new interface
performed with a low false positive rate and "modest" true positive
rate. On tests with four able-bodied subjects, the average false
positive rate was 0.1%, and the average true positive rate was 56.2%,
with one subject achieving rates as good as 0.0% and 64.2%,
respectively. The researchers speculate that the ratio between the two
rates is the best that has been reported for an EEG-based self-paced BCI
system.

In the future, the researchers plan to study how to improve the distinct
EEG pattern quality in the lower-performing subjects compared with the
higher quality distinctions in the higher-performing subjects. They also
want to explore methods to reduce interference caused by artifacts such
as eye movements, which can significantly affect the movement-related
potentials component of the system. Future studies will also investigate
how individuals with limited motor abilities can work with the
interface, as those individuals may benefit the most from brain-computer
interfaces.

"With the low false positive rates that we have achieved, we believe
implementing a self-paced EEG-based BCI system can be implemented for
practical applications in the next couple of years," said Fatourechi.
"We have to further improve the performance during the presence of
artifacts and carry out some online experiments using the proposed
system."