Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research

Area of Science:

• Neuroergonomics
• Human-Computer Interaction
• Cognitive Neuroscience

Background:

• Neuroergonomics aims to enhance human-machine interfaces by understanding brain activity and mental states.
• Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are key brain imaging techniques in this field.
• Combining EEG and fNIRS shows promise for improved mental state decoding in interfaces.

Purpose of the Study:

• To review studies comparing bimodal EEG-fNIRS with unimodal EEG and fNIRS for mental state decoding accuracy.
• To assess the generalizability of bimodal system improvements across different neuroergonomic paradigms.
• To consider the challenges and potential of wearable EEG-fNIRS systems in real-world contexts.

Main Methods:

• Systematic review of 33 studies comparing bimodal EEG-fNIRS against unimodal EEG or fNIRS.
• Analysis of mental state decoding accuracy across various neuroergonomic subdomains.
• Evaluation of studies considering practical application in naturalistic settings.

Main Results:

• Bimodal EEG-fNIRS consistently outperformed unimodal EEG or fNIRS in mental state decoding accuracy.
• Improvements were observed despite significant conceptual and methodological variations in the reviewed studies.
• Challenges remain in translating these findings to practical, real-world applications.

Conclusions:

• Bimodal EEG-fNIRS offers enhanced capabilities for designing advanced human-machine interfaces.
• Further research is needed to overcome limitations for widespread adoption in naturalistic environments.
• Future work should focus on optimizing wearable systems and standardizing methodologies for practical neuroergonomic applications.