Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator.

Area of Science:

• Cognitive Neuroscience
• Neurotechnology
• Human Factors Engineering

Background:

• Transferring lab findings to real-world tasks is difficult due to environmental complexity and bulky recording equipment.
• Inattentional deafness, a cognitive failure to perceive auditory stimuli, occurs in dynamic, complex environments.

Purpose of the Study:

• To investigate the feasibility of using low-density electroencephalography (EEG) systems to detect cognitive failures in realistic settings.
• To assess brain activity during induced inattentional deafness using a motion flight simulator.

Main Methods:

• A motion flight simulator was used to induce inattentional deafness to auditory alarms in expert participants.
• Two portable EEG systems, Enobio and cEEGrid, recorded brain activity.
• Brain activity was classified using Riemannian Geometry principles to differentiate between missed and detected alarms.

Main Results:

• Inattentional deafness was induced at a significantly higher rate (34.7%) compared to standard lab tasks (5%).
• Both EEG systems captured inattentional deafness-related brain activity in time, frequency, and time-frequency domains.
• Single-trial classification accuracy exceeded 70%, with cEEGrid reaching 71.5%.

Conclusions:

• Portable EEG systems can effectively record and classify brain activity associated with inattentional deafness.
• These findings support the development of systems for detecting cognitive failures in real-life scenarios, such as aviation.