Parallel Memory-Efficient Processing of BCI Data

被引：0

作者：

Alexander, Trevor ^{[1
]}

Kuh, Anthony ^{[1
]}

Hamada, Katsuhiko ^{[2
]}

Mori, Hiromu ^{[3
,4
]}

Shinoda, Hiroyuki ^{[2
]}

Rutkowski, Tomasz ^{[3
,4
]}

机构：

[1] Univ Hawaii Manoa, Dept Elect Engn, Honolulu, HI 96822 USA

[2] Univ Tokyo, Tokyo, Japan

[3] Univ Tsukuba, Life Sci Ctr TARA, Tsukuba, Ibaraki 305, Japan

[4] RIKEN, Brain Sci Inst, Wako, Saitama, Japan

来源：

2014 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA) | 2014年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Following after magnetic resonance imaging (MRI) and electrocortigraphy (ECoG), electroencephalography (EEG) based research is entering the world of big data[1]. 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.

引用

页数：9

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