Adaptive Linear Discriminant Analysis Algorithm Applied to Motion Signal Classification in EEG Processing

DOI

10.2991/icismme-15.2015.82How to use a DOI?

Keywords

EEG; brain computer interface; aCSP algorithm; aLDA algorithm; LDA algorithm; updating coefficient.

Abstract

In view of the current development research on electroencephalograph (EEG) brain computer interface (BCI), which shall not be limited to use linear discriminant analysis (LDA) algorithm only. The brain computer interface that uses multi channel and multi type EEG signal is gradually fusing on other ways that can better reflect the brain activity. Based on the EEG computer interface technology, we can use adaptive linear discriminant analysis (aLDA) algorithm and human-computer interaction method, also regulate adaptive updating coefficient (UC) to reflect the brain thinking activity better. In this paper, we use adaptive common spatial pattern (aCSP) algorithm to extract feature and better classification algorithm to process the EEG, which can improve the efficiency, accuracy and stability of signal classification.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rui Xu
AU  - Haoyue Tang
PY  - 2015/07
DA  - 2015/07
TI  - Adaptive Linear Discriminant Analysis Algorithm Applied to Motion Signal Classification in EEG Processing
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 412
EP  - 417
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.82
DO  - 10.2991/icismme-15.2015.82
ID  - Xu2015/07
ER  -