Browsing by Author "Alreshidi, Ibrahim"

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    Advancing aviation safety through machine learning and psychophysiological data: a systematic review
    (IEEE, 2024-01-03) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl W.
    In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.
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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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    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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    A comprehensive analysis of machine learning and deep learning models for identifying pilots’ mental states from imbalanced physiological data
    (AIAA, 2023-06-08) Alreshidi, Ibrahim; Yadav, Satendra; Moulitsas, Irene; Jenkins, Karl W.
    This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential.
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    Illuminating the neural landscape of pilot mental states: a convolutional neural network approach with Shapley Additive explanations interpretability
    (MDPI, 2023-11-11) Alreshidi, Ibrahim; Bisandu, Desmond Bala; Moulitsas, Irene
    Predicting pilots’ mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states—channelised attention, diverted attention, startle/surprise, and normal state—in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model’s interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection.
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    Multimodal approach for pilot mental state detection based on EEG
    (MDPI, 2023-08-23) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl W.
    The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach.
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    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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    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.