Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs

Copyright © 2020 Jiang, Wang, Wu, Qin, Xu and Yin..

Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Frontiers in human neuroscience - 14(2020) vom: 16., Seite 231

Sprache:

Englisch

Beteiligte Personen:

Jiang, Jing [VerfasserIn]
Wang, Chunhui [VerfasserIn]
Wu, Jinghan [VerfasserIn]
Qin, Wei [VerfasserIn]
Xu, Minpeng [VerfasserIn]
Yin, Erwei [VerfasserIn]

Links:

Volltext

Themen:

Brain–computer interface (BCI)
Common spatial pattern (CSP)
Electroencephalogram (EEG)
Feature selection
Journal Article
Motor imagery (MI)
Support vector machine (SVM)

Anmerkungen:

Date Revised 28.09.2020

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/fnhum.2020.00231

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM31290035X