Journal article
Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator
FRONTIERS IN NEUROERGONOMICS, v 2, 802486
06 Jan 2022
PMID: 38235232
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.
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Details
- Title
- Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator
- Publication Details
- FRONTIERS IN NEUROERGONOMICS, v 2, 802486
- Publisher
- FRONTIERS MEDIA SA; LAUSANNE
- Grant note
- The authors would like to acknowledge the Artificial and Natural Intelligence Toulouse Institute (ANITI) for currently funding BS and FD.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:001105176600001
- Scopus ID
- 2-s2.0-85133521291
- Other Identifier
- 991021860724804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Ergonomics
- Neurosciences