hannel selection methods for the P 300 Speller

The P300 Speller brain–computer interface (BCI) allows a user to communicate without muscle activity by reading electrical signals on the scalp via electroencephalogram. Modern BCI systems use multiple electrodes (“channels”) to collect data, which has been shown to improve speller accuracy; however, system cost and setup time can increase substantially with the number of channels in use, so it is in the user’s interest to use a channel set of modest size. This constraint increases the importance of using an effective channel set, but current systems typically utilize the same channel montage for each user. We examine the effect of active channel selection for individuals on speller performance, using generalized standard feature-selection methods, and present a new channel selection method, termed jumpwise regression, that extends the Stepwise Linear Discriminant Analysis classifier. Simulating the selections of each method on real P300 Speller data, we obtain results demonstrating that active channel selection can improve speller accuracy for most users relative to a standard channel set, with particular benefit for users who experience low performance using the standard set. Of the methods tested, jumpwise regression offers accuracy gains similar to the best-performing feature-selection methods, and is robust enough for online use. © 2014 Published by Elsevier B.V. 36 37 38 39 40 41 42 43 . Introduction Brain–computer interface (BCI) systems are designed to analyze eal-time data associated with a human user’s brain activity and ranslate it into computer output. The clearest current motivation or BCI development is to extend a means for communication and ontrol to people with neurological diseases, such as amyotrophic ateral sclerosis (ALS) or spinal-cord injury, who have lost motor Please cite this article in press as: Colwell KA, et al. Channel select http://dx.doi. org/10.1016/j.jneumeth. 2014.04. 009 bility (“locked-in” patients). However, state-of-the-art BCI sysems for such individuals are still expensive and limited in speed nd accuracy, and setup for home use is nontrivial; most systems ∗ Corresponding author. E-mail addresses: kenneth.colwell@duke.edu, ken.colwell@gmail.com (K.A. Colell), ryand1@goldmail.etsu. edu (D.B. Ryan), cst1@duke.edu (C.S. Throckmorton), ellers@mail.etsu. edu (E.W. Sellers), leslie.collins@duke.edu (L.M. Collins). ttp://dx.doi. org/10.1016/j.jneumeth. 2014.04. 009 165-0270/© 2014 Published by Elsevier B.V. 44 45 46 47 48 remain in the experimental stage and are primarily used in a laboratory environment (Vaughan et al., 2006). One BCI that has been successfully deployed to users with ALS (Sellers and Donchin, 2006) is the P300 Speller, first developed by Farwell and Donchin (1988). This system combines measurements of electroencephalogram (EEG) signals on the user’s scalp, a software signal processor, an online classifier, and presentation of stimuli that evoke a P300 event-related potential (ERP), in order to sequentially choose items from a list (e.g. the letters in a word, or commands such as “Page Down” or “Escape”). The original P300 Speller, as conceived by Farwell and Donchin, employed only a single electrode (one “channel” of information). The use of additional channels was discovered to improve clasion methods for the P300 Speller. J Neurosci Methods (2014), sification performance, and most if not all modern P300 Speller systems include data from multiple recording sites (e.g. Krusienski et al., 2006; Sellers and Donchin, 2006; Schalk et al., 2004). However, larger channel sets require more complicated electrode caps 49 50 51 52