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Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

Raza, Haider and Rathee, Dheeraj (2018) 'Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface.' In: Tanaka, Toshihisa and Arvaneh, Mahnaz, (eds.) Signal Processing and Machine Learning for Brain-Machine Interfaces. Institution of Engineering and Technology, 125 - 141. ISBN 9781785613982

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Nonstationary learning refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the nonstationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG)-based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 13 Feb 2019 11:58
Last Modified: 13 Feb 2019 12:15

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