Alreshidi, IbrahimMoulitsas, IreneJenkins, Karl2024-06-032024-06-032023-08-23Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl (2023). Code and Data: Multimodal Approach for Pilot Mental State Detection Based on EEG. Cranfield Online Research Data (CORD). Software. https://doi.org/10.17862/cranfield.rd.22232062https://dspace.lib.cranfield.ac.uk/handle/1826/21777Data: This folder contains: A dataset called Crews_equalized_dataset_epo.fif which was used to obtain the results presented in the journal paper. It is the preprocessed EEG dataset used to predict four mental states, Channelised Attention, Diverted Attention, Startle/Surprise, and Baseline. A dataset called Example_raw.fif which was used to obtain Figure 6 of the journal paper. Source code: This folder contains a jupyter notebook called python_code.ipynb which implements the proposed EEG preprocessing pipeline and all the algorithms presented and validated in the journal paper. Output: This folder contains: A figure called Confusion Matrices.jpg which shows results from the Random Forest classifier in (A), Extremely Randomized Trees in (B), Gradient Tree Boosting in (C), AdaBoost in (D), and Voting in (E). Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. A text file called ML models evaluation.txt which contains the results produced by all algorithms presented and validated in the journal paper. A figure called The preprocessed EEG signals.jpg which shows the EEG signals, upon completion of our preprocessing pipeline, fed into the machine learning models for training and testing purposes.MITEnsemble Learning''Machine Learning''EEG''Pilot Deficiencies''Artifact Detection''Tangent Space''EEG preprocessing''Heterogeneous Data''Mental States Classification''Feature Extraction'Software10.17862/cranfield.rd.22232062