An Exploration of BCI2000 Utility Across Multiple Sessions
Kimberly J. The1,2, Brittany K. Taylor1, Jewel Crasta1, Mei-Heng Lin1, Elliott M. Forney1, Charles W. Anderson1,
Patricia L. Davies1, William J. Gavin1
1Colorado
Introduction
 Brain computer interface (BCI) is a type of assistive
technology that uses noninvasive
electroencephalography (EEG) to record the neural
activity of the brain and convert it into alternative
outputs to run electronic devices.
 Because it only requires alteration of one’s mental
state, BCI is promising technology for individuals with
severe motor impairments and limited communication
abilities. 2
 This study utilizes the P300 speller module of the
BCI2000 software.
 BCI2000- a general purpose software available as
a standard platform to facilitate BCI research.3
 P300 speller- a BCI2000 module that allows
participants to communicate by spelling in real
time while bypassing motor control.
State University, 2University of Illinois- Chicago
Study Design
Results
Participants
Classifier Descriptives
 13 neurotypical adults, ages 18 to 49 years (M=
23.33, SD= 8.012).
Individual differences, channel distribution, and weight
contribute to variability across participants. See Figures 4
and 5.
All participants had posterior sites with maximum RMS
values, but only 6 of the13 participants had maximum
RMS values at frontal sites. See Figure 4.
Procedure
 Participants were asked to make 5 visits to the lab
within 6 days.
 The first part of each session involved a calibration
session to train the classifier to distinguish between
target and nontarget letters. See Session 0 in
Figure 2.
 The second part of each session involved spelling
words and phrases in real time. The pre and post
tests used the same two phrases. Words increased
in complexity across sessions. See Figure 2.
Figure 7: Pearson’s correlations of maximum RMS values
across sessions
Purpose, Research Question, and Hypothesis
 Purpose: 1) provide a basis for analyzing
performance of the P300 speller module of the
BCI2000 software across multiple sessions and 2)
assess usability of the P300 speller.
 Research Question: What are the effects of
practice on spelling accuracy for the P300 speller
module of the BCI2000 software?
 Hypothesis: Accuracy will remain steady or improve
with practice.
Figure 4: Frequency of channel use across all participants
9 out of 13 participants had only negative weights. See
Figure 5.
P300 Speller
 This study utilized a copy spelling function where the
word or phrase to be spelled was displayed at the top
of the screen. Spelling results were displayed directly
below. See Figure 1.
 Contained a 6x6 matrix whose rows and columns
flashed randomly at rapid rates. See Figure 1.
 Each row/column is illuminated for 125ms with a
125ms ISI.
 Rate is held constant across all trials at 1.33
characters spelled per minute.
 The target letter flashed 30 times during 180 flashes
per letter-trial for calibration & real-time classification.
Calibration
 A linear discriminant analysis (LDA) classified the
participant’s brain response to each flash as either a
target or a non-target letter.
 Calibration data were analyzed offline to determine
the input for the classifier. See Figure 3.
 A feature map of the data showed the intensity of
brain activity, called Root Mean Squared (RMS)
values, across time (in ms) and channels. 3 See
Figure 3.
 3 to 4 of the highest activity points were selected as
input to the LDA classifier.
 Event-related potential (ERP) plots based on RMS
selections showed the weight of the discrimination.
See Figure 3.
+1 means the target produced a positive
deflection.
-1 means the target produced a negative
deflection.
Figure 5: Average weights obtained at each channel for each
participant
Participants rated the average usability of the P300 speller
module of the BCI2000 system a 7 out of 10 with 0 = “not at
all usable” and 10 = “definitely usable.”
The BCI2000 is difficult to learn to setup and operate with
the Biosemi ActiveTwo EEG system.
The variability in our results indicate that it may be
necessary to calibrate each time a participant wants to use
the BCI2000. However, this is not a very practical option.
Figure 6: Percent accuracy of participants across 5 sessions.
5 participants improved their spelling accuracy, 7
participants saw a decrease in spelling accuracy and 1
participant’s spelling accuracy was the same on sessions
1 and 5. See Figure 6.
There were no significant improvements in spelling
accuracy between sessions 1 and 5, t(12) = 0.37, p =
.72, SD = 34.84.
Correlations showed that performance on subsequent
days was related. See Figure 7.
Figure 1: Layout of the P300 matrix speller
Biosemi ActiveTwo: Electrophysiological
Measurements
Accuracy of spelling with the P300 module of the BCI2000
software is variable with practice.
Spelling accuracy does not improve with practice.
This technology is called the P300 speller, however the
classifier more often used the N200 component rather than
the P300 component to differentiate between target and
nontarget letters.
If higher RMS values are obtained during calibration,
participants may achieve a higher percent accuracy of
spelling.
BCI2000 Usability
No Consistent Change in Spelling with Practice
 32 scalp sites, 2 bipolar eye monitors.
 Recorded at 1024 Hz sample rate.
Repeated measures ANCOVA: Percent correct on
session 5 accounted for a significant portion of the variance
in the maximum RMS values across the 5 sessions. , F(1,11)
= 9.49, p = .01, η2p = .46.
Linear regression showed that RMS values across all 5
sessions predicted spelling accuracy (% correct) on day 5,
F(5,12) = 5.62, p = .02. This model accounted for 80% of the
variance in percent correct on session 5, R2= .80, Adjusted
R2 = .66.
Discussion
Figure 2: Summary of study design
Technology and Software
Results (cont.)
Figure 3: Feature map and ERP plot of a participant’s calibration data.
Presented at the 54th annual meeting of the Society for Psychophysiological Research, Atlanta, GA, September 10th – 14th, 2014
References
1. Wolpaw J.R, Wolpaw, E, W. (2012). Brain computer interfaces: Something new under the
sun. In J.R Wolpaw, E.W. Wolpaw (Eds.) Brain computer interfaces: principles and practice.
New York: Oxford University.
2. Anderson, C., Davies, P. L., Gavin, W.J. (2011) Removing Barriers to the Practical Use of
Non-Invasive Brain-Computer Interfaces. Abstract for Grant Proposal to NSF. Colorado State
University. Fort Collins, CO.
3. Schalk G, Mellinger J. (2010). A practical guide to brain-computer interfacing with BCI2000:
General-purpose software for brain-computer interface research, data acquisition, stimulus
presentation, and brain monitoring. London: Springer.
Acknowledgements: Funded by National Science Foundation (NSF) Anderson, PI; Davies & Gavin, Co-PIs (IIS1065513) and generous support from the Department of Occupational Therapy through PRSE funds. We also thank
the clients and their caregivers and all the college students that have participated in this study.
Special thanks to Dr. Chuck Anderson and Elliott Forney, Department of Computer Science, for their contributions to
the information on this poster
Correspondence should be addressed to Patricia L. Davies or Kimberly J The, Colorado State University, 219
Occupational Therapy, Fort Collins, CO 80523.
E-mail: pdavies@cahs.colostate.edu or kimberly.j.the@gmail.com