Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146013
Title: Individual alpha peak frequency based features for subject dependent EEG workload classification
Authors: Lim, Wei Lun
Sourina, Olga
Wang, Lipo
Liu, Yisi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2016
Source: Lim, W. L., Sourina, O., Wang, L., & Liu, Y. (2016). Individual alpha peak frequency based features for subject dependent EEG workload classification. Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi:10.1109/SMC.2016.7844748
Abstract: The individual alpha peak frequency (IAPF) is an important biological indicator in Electroencephalogram (EEG) studies, with many research publications linking it to various cognitive functions. In this paper, we propose novel Power Spectral Density (PSD) alpha features based on IAPF to classify 2 and 4 levels of EEG multitasking workload data. When optimized IAPF was considered, a 1.55% and 1.56% increase in average accuracy for 48 subjects' data, with 35 and 33 subjects showing improvement was observed for 2 and 4 class cases respectively. This trend suggests that individual specific features are able to improve classification performance compared to generalized features for subject dependent cases. The proposed features, which incorporates the biological meaning of the IAPF and provides subject specific information, can be considered as a viable alternative to the general alpha power feature when designing novel subject dependent feature sets for BCI workload recognition applications.
URI: https://hdl.handle.net/10356/146013
DOI: 10.1109/SMC.2016.7844748
Rights: © 2016 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. The published version is available at: https://doi.org/10.1109/SMC.2016.7844748
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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