Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces.
This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was p...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 8; pp. 1709 - 1726 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137705923&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137705923 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2019 vid: 57 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137705923 137705923 NLM31127535 10.1007/s11517-019-01989-w NLM31127535 137705923 ppf: 1709 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. aug: au: Rodrigues, Paula G. Filho, Carlos A. Stefano Attux, Romis Castellano, Gabriela Soriano, Diogo C. affil: Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), São Bernardo do Campo, SP, Brazil sug: subj: Electroencephalography Methods Imagination Brain-Computer Interfaces Electroencephalography Hand Electrodes Research, Medical Foot Resource Databases Tongue Brain Physiology Motor Activity Physiology Signal Processing, Computer Assisted Electroencephalography Equipment and Supplies Clinical Assessment Tools Scales ab: This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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