Developing a logistic regression model with cross-correlation for motor imagery signal recognition
Paper
Paper/Presentation Title | Developing a logistic regression model with cross-correlation for motor imagery signal recognition |
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Presentation Type | Paper |
Authors | Li, Yan (Author), Wu, Jinglong (Author) and Yang, Jingjing (Author) |
Journal or Proceedings Title | Proceedings of the 2011 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2011) |
ERA Conference ID | 50435 |
Number of Pages | 6 |
Year | 2011 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781424493241 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICCME.2011.5876793 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5876793 |
Conference/Event | 2011 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2011) |
ICME International Conference on Complex Medical Engineering | |
Event Details | 2011 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2011) Event Date 22 to end of 25 May 2011 Event Location Harbin, China |
Event Details | ICME International Conference on Complex Medical Engineering CME |
Abstract | Classification of motor imagery (MI)-based electroencephalogram (EEG) signals is a key issue for the development of brain-computer interface (BCI) systems. The objective of this study is to develop an algorithm that can distinguish two categories of MI EEG signals. In this paper, we propose a new classification algorithm for two-class MI signals recognition in BCIs. The proposed scheme develops a novel crosscorrelation-based feature extractor, which is aided with a logistic regression model. The present method is tested on dataset IVa of BCI Competition III, which contain two-class MI data for five subjects. The performance is objectively computed using a k-fold cross validation (k=10) method on the testing set for each subject. The results of this study are compared with the recently reported eight methods in the literature. The results demonstrate that our proposed method outperforms the eight methods in terms of the average classification accuracy. |
Keywords | electroencephalogram (EEG); brain-computer interface (BCI); motor imagery (MI); cross-correlation technique; logistic regression model |
ANZSRC Field of Research 2020 | 400607. Signal processing |
400399. Biomedical engineering not elsewhere classified | |
461399. Theory of computation not elsewhere classified | |
Public Notes | © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
Okayama University, Japan | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q10vx/developing-a-logistic-regression-model-with-cross-correlation-for-motor-imagery-signal-recognition
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