Welcome to the Brain-Computer Interface
Laboratory at East Tennessee State University, directed by
Dr. Eric Sellers. The laboratory is located
within the
ETSU
College of Arts and Sciences, Department of Psychology.
Amyotrophic lateral sclerosis, commonly known as ALS or Lou
Gehrig's Disease, is a progressive neurodegenerative illness that
results in weakening of the connections between the brain and
body. In the late stages, these individuals have no ability to
move or speak, though for the most part they retain normal
cognitive function. This condition is referred to as "locked-in
syndrome" or LIS. Our lab studies how people can use electrical
activity recorded from the scalp, known as the
electroencephalogram or (EEG), to control computers for the
purposes of communication with others and control of their
environments. By recording EEG signals from the scalp, then
detecting specific features of the EEG activity, we can translate
their brain activity into actions. The primary function of
brain-computer interfaces (BCIs), as we see it, is to allow
people to regain the control and communication they have lost due
to paralysis caused by ALS or acute events such as brainstem
stroke.
Our research is dependent upon a variety of fields including
psychology, psychophysiology, cognitive neuroscience, computer
science, electrical engineering, rehabilitation engineering, and
neurology, to name a few. Initially, we test experimental
procedures and manipulations in the laboratory. Procedures that
yield the best results (in terms of speed and accuracy of
communication) are then translated to formats specifically
designed for people with severe motor disabilities. These
protocols are then tested with disabled individuals in the
laboratory, in the home, and in the hospital environment. Our
ultimate goal is to develop a
BCI system that is robust and portable
enough to meet the daily communication and social interaction
needs of severely disabled individuals.
The basic principles of any
BCI are as follows:
(Click image to enlarge)
The figure shows a schematic of the essential components of a
BCI system, illustrated as follows: 1)
Signal acquisition, the recording of the brain signal. This
signal is then digitized for analysis. 2) Signal processing, the
conversion of the raw signal into a useful device command. This
involves both feature extraction, the identification of
meaningful changes in the signal, and feature translation, the
conversion of those signal changes into a device command. 3)
Device output, the overt command or control functions that are
administered by the
BCI
system. These outputs can
range from word processing and communication to higher levels of
control such as driving a wheel chair or controlling a prosthetic
limb. All of these elements work in concert to give the user
control over his or her environment. (Modified from: Leuthardt et
al. (2006). The emerging world of motor neuroprosthetics: a
neurosurgical perspective.
Neurosurgery, Volume 59(1): 1-14.)
The brain signal that our lab is most interested in using for
BCI
control is called the P300
event-related potential (the P300-
BCI
). It was initially discovered by Sam Sutton et al (1965)
and was first used as a means of implementing a
BCI
by Larry Farwell &
Emanuel Donchin (1988). Since 1988 many scientific papers have
been published on the use of the P300 as a
BCI
. As you can see, in terms
of scientific discovery
BCI
research is still in its
infancy, and we expect to see many advances to help profoundly
disabled people in the future.
The standard 6x6 P300 speller matrix:
An example of a 6x6 P300 speller matrix
configured for a calibration exercise. At the top, the word "DOG"
is presented. The letter in parentheses (D) is the current target
letter. As rows and columns flash successively, the user is asked
to count how many times the letter 'D' (the target) flashes. This
results in a P300 response being generated each time the row or
column containing the target flashes. The twelve-flash series is
repeated a predetermined number of times. The responses for each
row and column are averaged, and a classifier is applied to
determine how closely each averaged response resembles the P300.
The intersection of the row and column with the highest
classification values is selected. In this case, the row and
column containing the target letter 'D' would be selected, and a
"D" would be presented as feedback to the user on the line below
the presented word "DOG" at the top of the matrix.
An example of a person severely disabled by ALS using a
BCI
in his home:
(Click image to enlarge)
The right panel shows a person using a
BCI
in his home. He is wearing
an eight channel electrode cap, which is used to record the EEG
signals that control the
BCI. The left panel shows a close-up
of his computer screen. The P300 speller is located on the right
of the screen, a text editor is located at the top left of the
screen, and a predictive speller program is located at the bottom
left of the screen. The predictive speller works by examining the
letters chosen for the current word and presenting shortcuts for
the most common words beginning with those letters. For example,
selecting "B-R-2" might result in typing out "B-R-AIN". This
setup increases the efficiency of the user's
communication.
Data Competition Information Coming Soon
Experiments posted on the ETSU SONA site
What can I expect from my visit to the lab?
ETSU Psychology Department
ETSU Psychology Research
BCI PRESS
03/09/2010
Watch a BCI segment on 60 Minutes!
Society for Neuroscience
Cerebral Palsy Information
Lab Photos: click to enlarge!
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Dr. Sellers' Birthday Cake |