Aerospace

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https://dspace.lib.cranfield.ac.uk/handle/1826/20713

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Browsing Aerospace by Author "Alreshidi, Ibrahim"

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    Code and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'
    (Cranfield University, 2023-09-18 16:40) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl; Yadav, Satendra
    Data: This folder contains: - A dataset called combined_df4, which contains the power spectral density features after employing SMOTE. - A dataset called combined_df5, which contains the power spectral density features after employing SMOTE and cosine similarity. Source code: This folder contains: - A jupyter notebook called AdaBoost.ipynb which was used to generate the results for the AdaBoost algorithm. - A jupyter notebook called CNN.ipynb which was used to generate the results for the CNN algorithm. - A jupyter notebook called CNN+LSTM.ipynb which was used to generate the results for the CNN+LSTMalgorithm. - A jupyter notebook called LSTM.ipynb which was used to generate the results for the LSTMalgorithm. - A jupyter notebook called FNN.ipynb which was used to generate the results for the FNN algorithm. - A jupyter notebook called Random_Forest.ipynb which was used to generate the results for the Random Forest algorithm. - A jupyter notebook called XGBoost.ipynb which was used to generate the results for the XGBoost algorithm.
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    ItemOpen Access
    Code and Data: Multimodal Approach for Pilot Mental State Detection Based on EEG
    (Cranfield University, 2023-08-23 15:04) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl
    Data: 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.
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    ItemOpen Access
    Supporting code and data for 'Miscellaneous EEG Preprocessing and Machine Learning for Pilots' Mental States Classification: Implications'
    (Cranfield University, 2023-09-18 16:10) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl
    Data: This folder contains: - A dataset called Pilot_5_CA_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention state in a non-flight environment. - A dataset called Pilot_5_DA_raw.fif, which contains the EEG data of a pilot when he was experiencing the diverted attention state in a non-flight environment. - A dataset called Pilot_5_SS_raw.fif, which contains the EEG data of a pilot when he was experiencing the startle/surprise state in a non-flight environment. - A dataset called Pilot_5_LOFT_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention, diverted attention, and startle/surprise state in a flight simulator environment. Source code: This folder contains: - A jupyter notebook called ICAAI_Conference.ipynb which was used to generate the results of the study. - A python file called cf_matrix which was used to plot the confusion matrix
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    ItemOpen Access
    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.