Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Wearable EEG systems are crucial for real-time brain activity monitoring.
• High-density dry electrodes offer a non-invasive and convenient approach for EEG acquisition.
• Online analysis of complex brain activity in dynamic environments remains a challenge.

Purpose of the Study:

• To present and evaluate a wearable high-density dry-electrode EEG system.
• To introduce an open-source software framework for online neuroimaging and cognitive state classification.
• To demonstrate the feasibility of real-time cortical connectivity analysis and classification using dry EEG.

Main Methods:

• Developed a 64-channel dry EEG system with wireless data streaming.
• Implemented a real-time software framework including artifact rejection, source localization, and effective connectivity inference.
• Utilized constrained logistic regression (ProxConn) for cognitive state classification from connectivity features.

Main Results:

• Simulations showed high accuracy (AUC = 0.97) for real-time cortical connectivity estimation.
• Response error classification using effective connectivity achieved above-chance performance.
• Cortical ERP-based classification showed comparable or superior performance to ProxConn depending on source localization method.

Conclusions:

• The wearable high-density dry EEG system and software framework are feasible for real-time analysis.
• This validated application addresses the need for robust wearable neuroimaging.
• The open-source pipelines have broad potential impact in research, medicine, and brain-computer interfaces.