Massé Eva, Bartheye Olivier, Fabre Ludovic
Centre de Recherche de l'Ecole de l'Air, Salon-de-Provence, France.
Front Neuroinform. 2022 Jun 16;16:904301. doi: 10.3389/fninf.2022.904301. eCollection 2022.
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 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.
诸如警报之类的相关声音有时会被不自觉地忽略，这种现象被称为无意失聪。这种现象发生在特定条件下，包括高工作量（即多任务处理）和/或认知疲劳。在航空领域，这样的错误可能会对飞行安全产生严重后果。本研究采用了一种奇偶数范式，让参与者在模拟飞行的生态环境中检测罕见声音。通过操纵认知疲劳和认知负荷来引发无意失聪，并通过脑电图（EEG）记录大脑活动。我们的结果表明，可以基于大脑活动的时频分析对警报遗漏和警报检测进行分类。当算法在所有参与者身上进行训练时，我们达到了76.4%的最高准确率；当算法针对一名参与者单独训练时，最高准确率达到了90.5%。根据我们的宏伟目标，这种方法可以借助可解释人工智能来开发高效且易于理解的被动式脑机接口，通过实时检测此类注意力失误来提高飞行安全，并向飞行员提供适当反馈，为他们提供可靠且丰富的人机交互。
