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 State University, 2University of Illinois- Chicago Introduction Participants 13 neurotypical adults, ages 18 to 49 years (M= 23.33, SD= 8.012). 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. 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. Study Design Results Results (cont.) 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. Classifier Descriptives 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. 9 out of 13 participants had only negative weights. See Figure 5. 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 7: Pearson’s correlations of maximum RMS values across sessions 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. 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. Figure 4: Frequency of channel use across all participants Discussion 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. 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. 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 2: Summary of study design Technology and Software 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. Figure 5: Average weights obtained at each channel for each participant Making bullets bl BCI2000 Usability Figure 6: Percent accuracy of participants across 5 sessions. 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. No Consistent Change in Spelling with Practice Figure 1: Layout of the P300 matrix speller Acknowledgements: Funded by National Science Foundation (NSF) Anderson, PI; Davies & Gavin, Co-PIs (IIS-1065513) 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 Biosemi ActiveTwo: Electrophysiological Measurements 32 scalp sites, 2 bipolar eye monitors. Recorded at 1024 Hz sample rate. 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