Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces

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 (BCIs). Multimodal BCIs have been able to gain significant traction given their 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 a discussion on the available literature, highlighting recent advances, challenges, and future directions for each of these modalities.