Aerospace

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Browsing Aerospace by Author "Bisandu, Desmond"

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    Supporting data and code for 'Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with SHAP Interpretability'
    (Cranfield University, 2024-01-08 16:35) Alreshidi, Ibrahim; Moulitsas, Irene; Bisandu, Desmond
    Data: This folder contains: - PSD (Power Spectral Density) features and labels datasets for individual pilots. These were leveraged to acquire the results presented in Table 2 of our article. For results pertaining to a specific pilot, two files are utilised to train our proposed model: "Pilot_i_EEG_band_power_features.npy" and "Pilot_i_events.npy". In these filenames, 'i' represents the pilot's unique ID number. The file "Pilot_i_EEG_band_power_features.npy" contains power spectral density features extracted from five distinct frequency bands: delta, theta, alpha, beta, and gamma. On the other hand, "Pilot_i_events.npy" contains the class labels indicating the mental state of the pilot: 0 for baseline, 1 for startle/surprise, 2 for channelized attention, and 3 for diverted attention. - A combined dataset named "EEG_band_power_features.npy", which comprises the PSD features for all pilots. Its corresponding class labels are found in the "all_events.npy" file. This combined dataset was instrumental in deriving the results published in our paper. Source code: This folder contains: - A jupyter notebook called EEG_Stats.ipynb which computes the PSD features using the original EEG data for each pilot. It also include the source code to compute the average power in each frequency band for each mental state and the average power in each frequency band for each EEG channel using the combined pilots dataset. - A jupyter notebook called Ind_pilot_conv_model.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for each pilot. - A jupyter notebook called all_pilots.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for all pilots. It also includes the source code to obtain the training accuracy and loss curves, compute the confusion matrix, and obtain the top 10 important features for each mental state. Output: This folder contains: - A figure called "The average power in each frequency band across pilots" which shows the average power in each frequency band for each mental state using the combined pilots dataset. - A figure called "Heatmap for the average power in each frequency band for EEG channels" which shows the average power in each frequency band for each EEG channel using the combined pilots dataset. - A figure called "Confusion Matrix" which shows the confusion matrix results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Accuracy and loss curve" which shows the training accuracy and loss curves results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Top 10 important features for NE class" which shows the top 10 important features for detecting the baseline state using the combined pilots dataset. - A figure called "Top 10 important features for SS class" which shows the top 10 important features for detecting the Startle/Surprise state using the combined pilots dataset. - A figure called "Top 10 important features for CA class" which shows the top 10 important features for detecting the Channelised Attention state using the combined pilots dataset. - A figure called "Top 10 important features for DA class" which shows the top 10 important features for detecting the Diverted Attention state using the combined pilots dataset. - A text file called "1D-CNN model evaluation" which contain the results produced by all the proposed 1D-CNN model presented and tested in the journal paper.