BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road
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BRAINSAFEDRIVE will develop a tool as attentional detectors that detect drivers’ mental state in terms of stress, cognitive load, sleepiness in real time during simulated and/or natural driving situations. Here, the project combines two necessary state of the art expertise’s: 1) the acquisition and analysis of cerebral signals i.e. Electroencephalography (EEG) and Electrooculography (EOG); 2) the application of artificial intelligence and machine learning algorithms. The drivers’ mental state will be correlated with vehicular parameters e.g. brake, speed, acceleration, lane chnges etc and classify the driving as "normal, healthy and safe” driver. Three actors involved in this bilateral i.e. Sweden and Italy collaboration are: 1) Dept. of Molecular Medicine (DMM) at Sapienza University, Italy with the expertise of cerebral signal analysis; 2) Intelligence Systems group at
Mälardalen University (MDH), Sweden with the expertise of AI and machine learning algorithms; 3) BrainSigns (BS), Italy as SME with the expertise of the measurement system of cerebral activity during driving. MDH and BS already are collaborating through a H2020 project SimuSafe. During 3 years a) the researchers at DMM will be educated on different algorithms of AI and machine learning from MDH; b) researchers at MDH will be benefited with advance signal processing on cerebral signals from DMM; c) the BS as SME company will be benefited through neuroscience-based algorithms derived from DMM and MDH.
| First Name | Last Name | Title |
|---|---|---|
| Mobyen Uddin | Ahmed | Professor |
| Shahina | Begum | Professor |
| Shaibal | Barua | Senior Lecturer |
| Mir Riyanul | Islam | Doctoral student |
Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data (Feb 2023) Mir Riyanul Islam, Mobyen Uddin Ahmed, Shahina Begum 15th International Conference on Agents and Artificial Intelligence (ICAART2023)
A Systematic Review of Explainable Artificial Intelligence in terms of Different Application Domains and Tasks (Jan 2022) Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum Applied Sciences (ApplSci)
A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation using Machine Learning (Aug 2020) Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Pietro Aricò , Gianluca Borghini , Gianluca Di Flumeri Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment) (Brain Sci)
Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification (Nov 2019) Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Gianluca Di Flumeri The 3rd International Symposium on Human Mental Workload: Models and Applications (H-WORKLOAD 2019)
Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology (Sep 2019) Mir Riyanul Islam, Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed Workshop on CBR in the Health Science (WHS ICCBR 2019)
Causality in Explainable AI for Medical Applications (May 2019) Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed SciLifeLab Science Summit 2019 (SciLifeLab)
