Advancing brain-computer interfaces through fNIRS and EEG integration

Brain-computer interfaces (BCIs) have emerged as transformative tools for bridging the gap between neural activity and external devices, enabling direct communication and control through brain signals. Recent advancements in non-invasive neuroimaging techniques, particularly functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), have opened new avenues for developing robust, portable, and user-friendly BCIs. These modalities offer complementary strengths: EEG provides high temporal resolution for capturing rapid neural dynamics, while fNIRS delivers spatially localized hemodynamic responses, reflecting deeper brain activity. Integrating these technologies holds immense potential for enhancing BCI performance, usability, and applicability across diverse domains, including healthcare, neurorehabilitation, assistive technologies, and human-machine interaction.

This Research Topic aims to showcase cutting-edge research and innovations in BCI development leveraging the synergistic integration of fNIRS and EEG. We seek contributions that explore novel methodologies, signal processing techniques, and computational models to harness the combined strengths of these modalities. Additionally, we encourage studies that address challenges such as improving signal quality, reducing noise, enhancing spatial and temporal resolution, and developing adaptive algorithms for real-time applications. Translational research demonstrating the practical implementation of fNIRS-EEG-based BCIs in clinical, industrial, or everyday settings is also of great interest.

We welcome original research articles, reviews, methodological advancements, and perspective pieces that contribute to the following topics (but are not limited to):

Novel signal acquisition and processing techniques for fNIRS and EEG integration

Hybrid BCI systems combining fNIRS and EEG for enhanced performance

Machine learning and deep learning approaches for fNIRS-EEG data fusion

Real-time BCI applications using fNIRS and EEG

Neuroadaptive systems and closed-loop BCI designs

Applications in neurorehabilitation, assistive technologies, and communication

BCIs for cognitive state monitoring and mental workload assessment

Wearable and portable fNIRS-EEG systems for everyday use

Challenges and solutions in multimodal BCI development

Ethical and societal implications of fNIRS-EEG-based BCIs

This Research Topic aims to foster interdisciplinary collaboration among researchers in neuroscience, engineering, computer science, and clinical practice, driving the development of next-generation BCIs that are more accurate, accessible, and impactful. By highlighting the latest advancements and addressing key challenges, we hope to accelerate the translation of fNIRS-EEG-based BCI technologies from the lab to real-world applications.

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Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: fNIRS, EEG, BCI, neural activity, brain signals, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.