Analysis of factors that influence the performance of biometric systems based on EEG signals

作者：

Highlights：

• SVM and Adaboost obtain the best electroencephalogram classification results.

• 1.75s of electroencephalogram recording is enough for classification.

• Classification depends on the decomposition levels of Discrete Wavelet Transform.

• The relative wavelet energy is a promising feature vector for biometrics.

摘要

•SVM and Adaboost obtain the best electroencephalogram classification results.•1.75s of electroencephalogram recording is enough for classification.•Classification depends on the decomposition levels of Discrete Wavelet Transform.•The relative wavelet energy is a promising feature vector for biometrics.

论文关键词：Biometrics,Electroencephalogram,Discrete Wavelet Transform,Performance factors

论文评审过程：Received 7 July 2020, Revised 1 September 2020, Accepted 2 September 2020, Available online 5 September 2020, Version of Record 11 September 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113967