Cracking the Code: Using AI to Detect Inattentional Deafness in Flight Simulator

Published on June 16, 2022

Imagine you’re in a flight simulator, multi-tasking like a pro, when suddenly, an alarm goes off. But wait! Your brain is overloaded, and you don’t even hear it. That’s called inattentional deafness, and it’s a big problem for flight safety. In this study, researchers manipulated cognitive fatigue and workload to trigger this phenomenon and recorded participants’ brain activity using electroencephalography (EEG). They found that they could classify alarm omission and detection by analyzing the time-frequency patterns of brain activity. By developing explainable artificial intelligence, they aim to create brain-computer interfaces that can detect attentional failures in real-time and provide valuable feedback to pilots. With the potential to improve flight safety, this research opens up exciting possibilities for human-machine interactions in aviation. Want to know more? Check out the full article!

Relevant sounds such as alarms are sometimes involuntarily ignored, a phenomenon called inattentional deafness. This phenomenon occurs under specific conditions including high workload (i.e., multitasking) and/or cognitive fatigue. In the context of aviation, such an error can have drastic consequences on flight safety. This study uses an oddball paradigm in which participants had to detect rare sounds in an ecological context of simulated flight. Cognitive fatigue and cognitive load were manipulated to trigger inattentional deafness, and brain activity was recorded via electroencephalography (EEG). Our results showed that alarm omission and alarm detection can be classified based on time-frequency analysis of brain activity. We reached a maximum accuracy of 76.4% when the algorithm was trained on all participants and a maximum of 90.5%, on one participant, when the algorithm was trained individually. This method can benefit from explainable artificial intelligence to develop efficient and understandable passive brain–computer interfaces, improve flight safety by detecting such attentional failures in real time, and give appropriate feedback to pilots, according to our ambitious goal, providing them with reliable and rich human/machine interactions.

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