Version 1
: Received: 9 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (14:39:52 CEST)
Version 2
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 13 September 2024 (04:21:29 CEST)
How to cite:
Ghosh, S.; Máthé, D.; Bhuvana, H. P.; Sankarapillai, P.; Mohan, A.; Bhuvanakantham, R.; Gulyás, B.; Padmanabhan, P. Review of Multimodal Data Acquisition Approaches for Brain-Computer Interfaces. Preprints2024, 2024060777. https://doi.org/10.20944/preprints202406.0777.v2
Ghosh, S.; Máthé, D.; Bhuvana, H. P.; Sankarapillai, P.; Mohan, A.; Bhuvanakantham, R.; Gulyás, B.; Padmanabhan, P. Review of Multimodal Data Acquisition Approaches for Brain-Computer Interfaces. Preprints 2024, 2024060777. https://doi.org/10.20944/preprints202406.0777.v2
Ghosh, S.; Máthé, D.; Bhuvana, H. P.; Sankarapillai, P.; Mohan, A.; Bhuvanakantham, R.; Gulyás, B.; Padmanabhan, P. Review of Multimodal Data Acquisition Approaches for Brain-Computer Interfaces. Preprints2024, 2024060777. https://doi.org/10.20944/preprints202406.0777.v2
APA Style
Ghosh, S., Máthé, D., Bhuvana, H. P., Sankarapillai, P., Mohan, A., Bhuvanakantham, R., Gulyás, B., & Padmanabhan, P. (2024). Review of Multimodal Data Acquisition Approaches for Brain-Computer Interfaces. Preprints. https://doi.org/10.20944/preprints202406.0777.v2
Chicago/Turabian Style
Ghosh, S., Balázs Gulyás and Parasuraman Padmanabhan. 2024 "Review of Multimodal Data Acquisition Approaches for Brain-Computer Interfaces" Preprints. https://doi.org/10.20944/preprints202406.0777.v2
Abstract
There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). Multi-modal BCIs have been able to gain significant traction given the potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities including Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Single-Unit Activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature highlighting recent advances, challenges, and future directions for each of these modalities.
Keywords
Brain Computer Interface; data acquisition modalities; functional near-infrared spectroscopy; electroencephalography; functional magnetic resonance imaging; multimodal integration; signal processing
Subject
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
