Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions

Abstract

Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the “brain at work” in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations.

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

“…On the other hand, humans can handle unexpected events, they have intuition, are creative and capable of making complex (e.g., ethical or moral) decisions. However, the human operator may experience high workload [6] and cognitive fatigue [7,8] that in turn can impair their attentional [9] and executive abilities [10]. In this cybernetic system context, several challenges have arisen such as the implementation of human-robots monitoring autonomously and the volunteers have to supervise the robot's status and manage the water tank supply level.…”

Spectral EEG-based classification for operator dyads’ workload and cooperation level estimation

“…In the same manner classifiers were trained to detect the cooperation level (always binary) for each subject, and for the team. To do so, a classification pipeline close to that used by Dehais and collaborators [12] and by Roy and collaborators [8] was used. The EEG data processing and classification were run using EEGLab (V14.1.2b) and Matlab (R2019a).…”

“…Using dedicated classification pipelines on various cerebral measures several authors have been able to efficiently estimate an operator's mental workload in laboratory [3], [8], [9] or even in real life settings [10]- [12]. Despite their interest, most of these studies have focused on the monitoring of a single operator.…”

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

“…Taking into account the nature of the features, there are countless different examples of configurations, in terms of number of channels and frequencies used in the literature. The number of electrodes can vary from 64 [24], [36] to 6 [37], and even the bands considered vary from 2 (Theta and Alpha, [9]), to 7 (0-4 Hz, 4-7 Hz, 7-12 Hz, 12-30 Hz, 30-42 Hz, 42-84 Hz, 84-128 Hz [38]), up to considering all the single frequency bins that define the spectrum [39]. Several studies have shown that it does not necessarily take more than 5-10 electrodes to classify the workload [24].…”

“…In fact, if the analysis shows that some electrodes are not useful for the classification of the workload, it is possible to remove them and then make the instrumentation lighter and less invasive. In this case the most used methods for the selection of features are those recursive, such as recursive feature elimination [18], [41], sequential forward feature selection [24] or methods that take into account the dependence between features such as the Minimum Redundancy Maximum Relevance selection [37], [42], [43], or unsupervised method (Locally linear embedding, [44]). Once defined a meaningful set of features, it is necessary to choose a model to define the workload level.…”