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Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-0730-4405
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
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2019 (English)In: Communications in Computer and Information Science, Volume 1107, 2019, p. 121-135Conference paper, Published paper (Refereed)
Abstract [en]

In the pursuit of reducing traffic accidents, drivers' mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers' MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by CNN-AE and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy.

Place, publisher, year, edition, pages
2019. p. 121-135
Keywords [en]
Autoencoder, Convolutional Neural Networks, Electroencephalography, Feature Extraction, Mental Workload
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45059DOI: 10.1007/978-3-030-32423-0_8Scopus ID: 2-s2.0-85075680380ISBN: 9783030324223 (print)OAI: oai:DiVA.org:mdh-45059DiVA, id: diva2:1344975
Conference
The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the roadAvailable from: 2019-08-22 Created: 2019-08-22 Last updated: 2024-04-15Bibliographically approved
In thesis
1. Explainable Artificial Intelligence for Enhancing Transparency in Decision Support Systems
Open this publication in new window or tab >>Explainable Artificial Intelligence for Enhancing Transparency in Decision Support Systems
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Artificial Intelligence (AI) is recognized as advanced technology that assist in decision-making processes with high accuracy and precision. However, many AI models are generally appraised as black boxes due to their reliance on complex inference mechanisms.  The intricacies of how and why these AI models reach a decision are often not comprehensible to human users, resulting in concerns about the acceptability of their decisions. Previous studies have shown that the lack of associated explanation in a human-understandable form makes the decisions unacceptable to end-users. Here, the research domain of Explainable AI (XAI) provides a wide range of methods with the common theme of investigating how AI models reach to a decision or explain it. These explanation methods aim to enhance transparency in Decision Support Systems (DSS), particularly crucial in safety-critical domains like Road Safety (RS) and Air Traffic Flow Management (ATFM). Despite ongoing developments, DSSs are still in the evolving phase for safety-critical applications. Improved transparency, facilitated by XAI, emerges as a key enabler for making these systems operationally viable in real-world applications, addressing acceptability and trust issues. Besides, certification authorities are less likely to approve the systems for general use following the current mandate of Right to Explanation from the European Commission and similar directives from organisations across the world. This urge to permeate the prevailing systems with explanations paves the way for research studies on XAI concentric to DSSs.

To this end, this thesis work primarily developed explainable models for the application domains of RS and ATFM. Particularly, explainable models are developed for assessing drivers' in-vehicle mental workload and driving behaviour through classification and regression tasks. In addition, a novel method is proposed for generating a hybrid feature set from vehicular and electroencephalography (EEG) signals using mutual information (MI). The use of this feature set is successfully demonstrated to reduce the efforts required for complex computations of EEG feature extraction.  The concept of MI was further utilized in generating human-understandable explanations of mental workload classification. For the domain of ATFM, an explainable model for flight take-off time delay prediction from historical flight data is developed and presented in this thesis. The gained insights through the development and evaluation of the explainable applications for the two domains underscore the need for further research on the advancement of XAI methods.

In this doctoral research, the explainable applications for the DSSs are developed with the additive feature attribution (AFA) methods, a class of XAI methods that are popular in current XAI research. Nevertheless, there are several sources from the literature that assert that feature attribution methods often yield inconsistent results that need plausible evaluation. However, the existing body of literature on evaluation techniques is still immature offering numerous suggested approaches without a standardized consensus on their optimal application in various scenarios. To address this issue, comprehensive evaluation criteria are also developed for AFA methods as the literature on XAI suggests. The proposed evaluation process considers the underlying characteristics of the data and utilizes the additive form of Case-based Reasoning, namely AddCBR. The AddCBR is proposed in this thesis and is demonstrated to complement the evaluation process as the baseline to compare the feature attributions produced by the AFA methods. Apart from generating an explanation with feature attribution, this thesis work also proposes the iXGB-interpretable XGBoost. iXGB generates decision rules and counterfactuals to support the output of an XGBoost model thus improving its interpretability. From the functional evaluation, iXGB demonstrates the potential to be used for interpreting arbitrary tree-ensemble methods.

In essence, this doctoral thesis initially contributes to the development of ideally evaluated explainable models tailored for two distinct safety-critical domains. The aim is to augment transparency within the corresponding DSSs. Additionally, the thesis introduces novel methods for generating more comprehensible explanations in different forms, surpassing existing approaches. It also showcases a robust evaluation approach for XAI methods.

Place, publisher, year, edition, pages
Västerås: Mälardalen university, 2024
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 397
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-64909 (URN)978-91-7485-626-2 (ISBN)
Public defence
2024-01-30, Gamma, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2023-12-04 Created: 2023-12-01 Last updated: 2024-01-09Bibliographically approved

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Islam, Mir RiyanulBarua, ShaibalAhmed, Mobyen UddinBegum, Shahina

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