CenEEGs

Author:

Dai Chenglong1,Pi Dechang1,Becker Stefanie I.2,Wu Jia3,Cui Lin4,Johnson Blake3

Affiliation:

1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. The University of Queensland, Brisbane, Queensland, Australia

3. Macquarie University, Sydney, New South Wales, Australia

4. Suzhou University, Suzhou, China

Abstract

This article explores valid brain electroencephalography (EEG) selection for EEG classification with different classifiers, which has been rarely addressed in previous studies and is mostly ignored by existing EEG processing methods and applications. Importantly, traditional selection methods are not able to select valid EEG signals for different classifiers. This article focuses on a source control-based valid EEG selection to reduce the impact of invalid EEG signals and aims to improve EEG-based classification performance for different classifiers. We propose a novel centroid-based EEG selection approach named CenEEGs, which uses a scale-and-shift-invariance similarity metric to measure similarities of EEG signals and then applies a globally optimal centroid strategy to select valid EEG signals with respect to a similarity threshold. A detailed comparison with several state-of-the-art time series selection methods by using standard criteria on 8 EEG datasets demonstrates the efficacy and superiority of CenEEGs for different classifiers.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Funding of Jiangsu Innovation Program for Graduate Education

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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