Parallel memory-efficient processing of BCI data

Author

Alexander, Trevor ; Kuh, Anthony ; Hamada, Katsuhiko ; Mori, Hiromu ; Shinoda, Hiroyuki ; Rutkowski, Tomasz

Author_Institution

Dept. of Electr. Eng., Univ. of Hawai´i at Manoa, Honolulu, HI, USA

fYear

2014

fDate

9-12 Dec. 2014

Firstpage

1

Lastpage

9

Abstract

Following after magnetic resonance imaging (MRI) and electrocortigraphy (ECoG), electroencephalography (EEG)-based research is entering the world of big data[l]. A research-quality brain-computer interface (BCI) data set can easily number in the hundreds of millions of points, making methodology of processing and classification critical. A selection of broadly applicable optimization methods implemented in R is presented that enables users to take advantage of parallelization, guaranteed call-by-reference to limit memory overhead, and scalable performance with common BCI processing tasks. As proof of concept, classification results for a P300 experiment and performance statistics are presented.

Keywords

brain-computer interfaces; electroencephalography; medical signal processing; parallel processing; signal classification; statistical analysis; BCI data processing; ECoG; EEG-based research; MRI; P300 experiment; brain-computer interface; call-by-reference; data classification; electrocortigraphy; electroencephalography; magnetic resonance imaging; parallel memory-efficient processing; performance statistics; Benchmark testing; Electroencephalography; Layout; Optimization; Runtime; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA)

Conference_Location

Siem Reap

Type

conf

DOI

10.1109/APSIPA.2014.7041779

Filename

7041779