@inproceedings{08f17a82f214491789755849f1b99113,
title = "A multiple autocorrelation analysis method for motor imagery EEG feature extraction",
abstract = "A novel multiple autocorrelation method for single trial EEG feature extraction was proposed. The time courses of ERD/ERS during motor imagery were investigated by calculating multiple autocorrelation before power spectrum analysis. Then the averaged power spectrums on specific frequency bands were sent to a K-nearest classifier to validate the separability between different classes. Compared with the result of power spectrum, the multiple autocorrelation performed better in attenuating noise and enhancing the separability between different classes with a small quantity of electrodes (C3 and C4). The maximum 90.0\% accuracy tested on dataset of BCI-competition 2003 for motor imagery is achieved.",
keywords = "BCI, motor imagery, multiple autocorrelation, signal separability",
author = "Xiangzhou Wang and An Wang and Shuhua Zheng and Yingzi Lin and Mingxin Yu",
year = "2014",
doi = "10.1109/CCDC.2014.6852688",
language = "English",
isbn = "9781479937066",
series = "26th Chinese Control and Decision Conference, CCDC 2014",
publisher = "IEEE Computer Society",
pages = "3000--3004",
booktitle = "26th Chinese Control and Decision Conference, CCDC 2014",
address = "United States",
note = "26th Chinese Control and Decision Conference, CCDC 2014 ; Conference date: 31-05-2014 Through 02-06-2014",
}