Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/8913
Title: Evaluation of synchronization measures for capturing the lagged synchronization between EEG channels: A cognitive task recognition approach
Authors: Olcay, Bilal Orkan
Karaçalı, Bilge
Keywords: EEG
Brain connectivity
Synchronization measures
Cognitive task recognition
FLD
Mutual information
Phase locking value
Cosine based similarity
Cross correlation
Nonlinear interdependency
Cross correntropy
Issue Date: 2019
Publisher: Elsevier Ltd.
Abstract: During cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III IVa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-III dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-III dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.
Description: Olcay, Bilal Orkan/0000-0003-3721-6756
WOS: 000495520100012
PubMed: 31561099
URI: https://doi.org/10.1016/j.compbiomed.2019.103441
https://hdl.handle.net/11147/8913
ISSN: 0010-4825
1879-0534
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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