OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities...

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Veröffentlicht in:Bioengineering Jg. 10; H. 5; S. 608
Hauptverfasser: Ali, Muhammad Umair, Kim, Kwang Su, Kallu, Karam Dad, Zafar, Amad, Lee, Seung Won
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 18.05.2023
MDPI
Schlagworte:
Accuracy
Algorithms
binary enhanced whale optimization algorithm
Bioengineering
Biology (General)
Brain
Brain research
Classification
Complementarity
Computation
Computer applications
Cost function
Data acquisition
Data integration
Datasets
EEG
Electroencephalography
Feature selection
fNIRS
Human-computer interaction
Human-computer interface
hybrid BCI
Implants
Infrared spectra
Infrared spectroscopy
Medical imaging
Near infrared radiation
Neuroimaging
Neurosciences
optimal feature selection
optimal feature selection; hybrid BCI; binary enhanced whale optimization algorithm; fNIRS; EEG
Optimization
Performance evaluation
QH301-705.5
Spectroscopy
Support vector machines
T
Technology
South Korea
hybrid BCI
binary enhanced whale optimization algorithm
fNIRS
optimal feature selection
EEG
ISSN:2306-5354, 2306-5354
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Zusammenfassung:Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.
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These authors contributed equally to this work.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering10050608