title: On optimal channel configurations for SMR-based brain-computer interfaces.
abstract: One crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.
authors.affiliation: Machine Learning Laboratory, Berlin Institute of Technology, Franklinstrasse 28/29, 10587, Berlin, Germany. claudia.sannelli@tu-berlin.de
authors.forename: Claudia
authors.initials: C
authors.lastname: Sannelli
authors.forename: Thorsten
authors.initials: T
authors.lastname: Dickhaus
authors.forename: Sebastian
authors.initials: S
authors.lastname: Halder
authors.forename: Eva-Maria
authors.initials: EM
authors.lastname: Hammer
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D005246:Feedback; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology; D008297:Male; D009043:Motor Activity / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface