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Learning with covariate shift-detection and adaptation in non-stationary environments : Application to brain-computer interface

Raza, H, Cecotti, H, Li, Y and Prasad, G 2015, 'Learning with covariate shift-detection and adaptation in non-stationary environments : Application to brain-computer interface' , in: Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers (IEEE), Article number 7280742.

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Abstract

Learning in the presence of dataset shifts in non-stationary environments is a major challenge. Dataset shifts in the form of covariate shifts commonly occur in a broad range of real-world systems such as, electroencephalogram (EEG) based brain-computer interfaces (BCIs). Under covariate shifts, the properties of the input data distribution may shift over time from training to test/operating phase. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and adaptation methodology, and its application to motor-imagery based BCIs. An exponential weighted moving average (EWMA) model based test is used for the covariate shift-detection in the features of EEG signals. The proposed algorithm initiates the adaptation by reconfiguring the knowledge-base of the classifier. Its performance is evaluated through experiments using a real-world dataset i.e. BCI Competition IV dataset 2A. Results show that the proposed method effectively performs covariate-shift-detection and adaptation and it can help to realize adaptive BCI systems.

Item Type: Book Section
Additional Information: International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12 July 2015 through 17 July 2015
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Joint Conference on Neural Networks (IJCNN) 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
ISBN: 978147991964
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:58
Last Modified: 19 Jan 2016 16:08
URI: http://usir.salford.ac.uk/id/eprint/35775

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