DocumentCode :
638780
Title :
Common Tensor Discriminant Analysis for human brainwave recognition accelerated by massive parallelism
Author :
Gajdos, Petr ; Dohnalek, Pavel ; Bobrov, Pavel
Author_Institution :
Dept. of Comput. Sci., VrB - Tech. Univ. of Ostrava, Ostrava, Czech Republic
fYear :
2013
fDate :
12-14 Aug. 2013
Firstpage :
189
Lastpage :
193
Abstract :
In this paper, a massively parallel implementation of Common Tensor Discriminant Analysis is presented with applications to human brainwave pattern recognition. The implementation, accelerated by the NVIDIA Compute Unified Device Architecture technology, is shown to be 11.49x faster than the original MATLAB version. Before processing by the discriminant analysis, the data is segmented by a sliding window and converted into the time-frequency domain by the continuous wavelet transform.
Keywords :
brain-computer interfaces; electroencephalography; medical signal processing; parallel architectures; pattern recognition; tensors; time-frequency analysis; wavelet transforms; MATLAB version; NVIDIA compute unified device architecture technology; common tensor discriminant analysis; continuous wavelet transform; human brainwave pattern recognition; human brainwave recognition; massive parallelism; massively parallel implementation; sliding window; time-frequency domain; Algebra; Europe; Graphics processing units; Libraries; MATLAB; Mesons; BCI; CTDA; parallelism; pattern matching; tensor;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on
Conference_Location :
Fargo, ND
Print_ISBN :
978-1-4799-1414-2
Type :
conf
DOI :
10.1109/NaBIC.2013.6617860
Filename :
6617860
Link To Document :
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