EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-controlled driving systems rely on accurate electroencephalography (EEG) signal interpretation.
• System reliability is challenged by potential electrode failures, which can corrupt data and degrade performance.
• Existing methods often fail to account for real-world signal degradation, such as electrode detachment.

Purpose of the Study:

• To develop and validate a novel EEG-based system for detecting driver emergency braking intentions.
• To address the challenge of single electrode failure within brain-controlled driving systems.
• To ensure robust and accurate driver intention prediction despite signal loss.

Main Methods:

• Electrode falling-off detection using EEG potentials.
• Multivariate linear regression for estimating missing EEG signals from available channels.
• Linear decoder for classifying driver intentions based on processed EEG data.

Main Results:

• High average accuracy (99.63%) in discriminating electrode falling-off events.
• Accurate signal estimation with an average correlation coefficient of 0.90 and RMSE of 11.43 μV.
• Significantly improved intention prediction accuracy (95.12%) with electrode failure compared to original methods (79.11%), nearing normal operation accuracy (95.95%).

Conclusions:

• The proposed method effectively detects and compensates for single electrode failures in EEG-based driver intention detection.
• This approach maintains high system accuracy, crucial for reliable brain-controlled driving applications.
• The findings suggest a significant advancement in the robustness of brain-computer interfaces for automotive safety.