Optimization of electrode channels in brain computer interfaces

• Published in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2009; pp. 6477 - 6480
• Main Authors: Kamrunnahar, M., Dias, N.S., Schiff, S.J.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2009
• Subjects: Application software; Brain computer interfaces; Computer interfaces; Electrodes; Electroencephalography; Electroencephalography - instrumentation; Electroencephalography - methods; Equipment Design; Equipment Failure Analysis; Evoked Potentials, Motor - physiology; Humans; Imagination - physiology; Linear discriminant analysis; Motor Cortex - physiology; Movement - physiology; Neural engineering; Principal component analysis; Reproducibility of Results; Scalp; Sensitivity and Specificity; User-Computer Interface
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2009.5333585

What is the optimal number of electrodes one can use in discrimination of tasks for a brain computer interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.