# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 7 Conclusions  
In this paper, we have presented two different approaches to the fusion of EEG sensor signals. Models were trained and validated on self-designed games (one serious game and one flight simulator with specific scenarios) to ensure unambiguous annotations. Models were trained and validated on the serious game using a one-subject-out scheme; and simulator data gathered from a subject not included in the training data were used to evaluate transfer capability. Results show that between the two models, projecting convolutional feature channels achieved higher performance, with 76.25% sensitivity and 87.81% specificity in WL detection in n-back-test leave-one-out subject evaluation and good task transfer with the detected WL increasing with the number of interruptions. Although these results provide evidence of the ability of the EEG sensor to discern between more and less demanding tasks--increasing the evidence the robustness of the EEG and its ability to transfer tasks--the fact that the 3-class problem BLs_WL2_WL3 does not correlate with flight complexity suggests the following improvements. A delicate issue that has an impact on the performances of methods is the filtering of signals required to remove muscular motion peaks and other artifacts. EEG pre-processing approaches have not been standardized, and even small changes in the artifact removal strategy may result in differences with large effects on particular portions of the signal. In this study, we have adopted a filtering approach based on signal probabilistic distribution for outlier removal in the temporal space. We consider that muscular motion could be filtered calibrating muscular signals before test recording to set either the values or the frequency ranges associated with muscular motion. Some studies claim the importance of considering multiple aspects of a user's state when developing cognitive state detection algorithms [45]. Consequently, affective state should be considered. Given that the way EEG sensors are fused has a direct impact in performance of models, alternative architectures should be further investigated. In this context, a direct improvement would be to consider ensemble models processing each sensor separately with own-learned weights. Furthermore, more recent architectures such as convolutional/LSTM and Lambda Nets that include attention modeling should be also studied. Conceptualization, D.G. and M.A.P. ; methodology, P.F., A.H.-S. and D.G. ; software, P.F. and J.Y. ; validation, J.Y., P.F., A.H.-S. and D.G. ; formal analysis, A.H.-S. and D.G. ; investigation, P.F., A.H.-S. and D.G. ; resources, M.A.P. ; data curation, J.Y. and P.F. ; writing--original draft preparation, J.Y. and A.H.-S.; writing--review and editing, A.H.-S. and D.G. ; visualization, J.Y. and A.H.-S.; supervision, D.G., A.H.-S. and M.A.P. ; project administration, M.A.P. and D.G. ; funding acquisition, M.A.P. All authors have read and agreed to the published version of the manuscript. This research was partially funded by Cleansky, grant number 831993, Ministerio de Ciencia e Innovacion (MCI), Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), RTI2018-095209-B-C21 (MCI/AEI/FEDER, UE); Agencia de Gestio d'Ajuts Universitats i de Recerca grant numbers 2017-SGR-1597 and 2017-SGR-1624; and CERCA Programme/Generalitat de Catalunya. Ethical review and approval were waived for this study, because data collected was not identifiable human material (the only data collected are anonymised EEG signals without any other information). Even so, participants were provided with information on the purpose, and on the content of the research. They were also given the choice to participate by agreeing to this information, or to not participate, and could quit the experiments at any moment. All this is in line with regulations on the use of personal information in scientific research in Spain. Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.