1. IntroductionMethods Participants  7 adults with severe motor impairment performed EEG recording sessions in their own homes.  9 adults with no motor impairment performed EEG recording sessions in a laboratory setting. P300 Speller Paradigm  In the present study, we are investigating an adaptation on the single letter, serial P300 speller paradigm.  We chose to use a serial P300 speller because it does not require any shift in eye gaze, thus it is likely that the resulting event-related potentials (ERPs) are due to the flash of the stimuli  Participants were seated comfortably in front of an LCD computer screen.  Letter characters flashed in the center of the screen one at a time with a stimulus duration of 100ms.  There was an inter-stimulus interval of 750ms between each letter presentation.  Participants were instructed to count the number of presentations of a predetermined target letter (p, b, or d).  There were 20 target characters out of 80 total presentations in each experiment block.  An additional 20 characters were “foils” that were not visually similar to the target letters (e.g., x, r, s)  Participants performed the task in three blocks – one block per target letter. Electrophysiological Procedures  EEG system: BioSemi ActiveTwo  32 scalp sites, 2 bipolar eye monitors  Recorded with a 1024 Hz A/D sampling rate  Re-referenced offline to averaged earlobes sites  Filtered with 0.23 to 30 Hz band pass (12dB/octave)  Segmented -200 to 700ms from stimulus onset  Baseline corrected from -200 to 0ms  Eye blinks were removed using a regression procedure  EOG artifact rejection (±100µV) Template-Matching Classification  A template was created for each target letter and for the foils in each block by producing an averaged ERP of the first 10 segments of each letter-type  ERPs were computed for Fz, Cz, and Pz sites  For each block, every individual segment was then compared to the target and non-target templates using two techniques discussed next (see Figure 2) Results A Simple Classification Routine for Event-Related Brain-Computer Interfaces What is BCI  Brain-computer interfaces (BCI) are used to establish a direct channel of communication between a user’s brain and a computer system. 1  Using electroencephalography (EEG), users can simply alter their mental state in order to operate various computerized devices, such as remote controls, motorized wheelchairs, and communication devices.  This is ideal for individuals with severe motor impairments because users only have to alter their mental state to operate the device. P300 Speller  A serial P300 speller operates by presenting a series of flashing characters, in this case letters, to the user (see Figure 1).  Ideally, a computer algorithm should be able to classify any given trial as a target or a non- target using as little computing time as possible.  Accomplishing this goal on a single-trial basis is important for BCI users to accomplish tasks in real-time as efficiently as possible. 1  A number of computer algorithms have been developed to classify the brain responses as target vs. non-target, but with varying success. Purpose  The purpose of this study was to develop a simple classifier that can distinguish brain activity elicited by target stimuli from that elicited by non-target stimuli on a single-trial basis. Brittany K. Taylor,¹ Camia Breeding, 1 Elliott M. Forney, 2 Charles W. Anderson, 2 Patricia L. Davies, 3 William J. Gavin 1 From the Departments of Human Development & Family Studies, 1 Computer Science, 2 and Occupational Therapy 3 Colorado State University, Fort Collins, CO, USA Classification Accuracy  The training data (segments included in the template) were correctly classified as “Target” or “Non-Target” trials at least 84% of the time (see Table 1).  Test data (segments not included in the template) were not classified as well with the highest accuracy reaching only 74%.  The variance and correlation routines each seemed to perform similarly with the training data and with the test data. Simple Classifier May be Too Simple  The variance and correlation template-matching classifiers only performed slightly above chance on the test data across all participants.  Such low accuracy is not ideal for real-world settings, thus the simple classification techniques would require more fine-tuning to be useful in practical applications.  It is possible that the simple correlation and variance measurements are too simplistic for complex brain data from EEG, and that classifiers that examine more features of ERPs could perform better. Individual Differences  Examination of individual results showed variable success of the classifier with test data accuracy ranging from 20% to 90% correct.  It is possible that individual differences in attention to the task may contribute to classifier success Classification Techniques  Adjusting each individual segment using an estimated signal-to-noise ratio may improve the accuracy of the classifier proposed in this study.  Classifiers that explore more features of the ERP like linear discriminant analysis 4 and echo state networks 5 are reporting higher success rates than the simple classification techniques proposed in this study. Conclusions Future Directions Brainwaves Research Lab Figure 1. Serial P300 speller Figure 2. Each individual foil segment (left) and target “P” segment (right) being compared to the “P” target averaged template and the “Foil” averaged template at site Cz Table 1. Percentage of segments that were correctly classified as “Target” or “Non-Target” using variance and correlation template-matching routines with training and test data averaged across all subjects. References 1.Blankertz, B., Lemm, S., Treder, M., Haufe, S., and Müller, K. (2011). Single-trial analysis and classification of ERP compenentsaturial. NeuroImage, 56, 814-825. 2.Chapman, R. M., & Bragdon, H. R. (1964). Evoked responses to numerical and non- numerical visual stimuli while problem solving. 3.Townsend, G. G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, C. K., &... Sellers, E. W. (2010). A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns. Clinical Neurophysiology, 121(7), 1109-1120. doi:10.1016/j.clinph.2010.01.030 4.Nand Sharma (2013) Masters Thesis, Department of Computer Science, Colorado State University, Fort Collins, CO. 5.Forney, E., Anderson, C., Gavin, W., and Davies, P. In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, June 3 - 7, 2013. Graz University of Technology Publishing House. DOI:10.3217/978-3-85125-260-6-148 Acknowledgements This research was funded by the National Science Foundation, grant number 1065513, and by the Colorado State University Occupational Therapy Department. TargetNon-Target VarianceCorrelationVarianceCorrelation TrainTestTrainTestTrainTestTrainTest B Target85.3353.3387.3354.0088.0058.6788.0056.00 D Target88.0053.3388.6753.3386.6752.0088.0053.33 P Target84.6755.3390.0068.0090.0074.0086.6760.00  The user attends to or counts only the character to be selected among all characters displayed. The P300 is larger for target characters than for non-target characters. 2, 3 Classifier Techniques  Minimum variance:  For every data point from 0-700ms (716 data points), a voltage difference between the segment and the averaged target template was calculated for the three electrode sites. This procedure was repeated for the averaged non-target (foil) template as well.  The variance scores of obtained for the target and foil comparisons was then averaged across the three electrode sites.  The segment was classified as “target” or “non-target” based on which comparison produced the smaller variance value  Maximum correlation:  Voltage values of data points from 0-700ms (716 data points) of the segment were correlated to voltage values of the target template of the same time period for each of the three electrode sites. This procedure was repeated for the non- target (foil) averaged template.  The correlations were averaged across the three electrode sites  The segment was classified as “target” or “non-target” based on which of the two had the maximum correlation value Presented at the 12 th Annual Meeting of the Front Range Neuroscience Group, Fort Collins, CO, December 10 th, 2014 “Foil” Segments “P” Target Template “Foil” Template “Foil” Segments “P” Target Template “Foil” Template