Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks

Benerradi, Johann, Maior, Horia A., Marinescu, Adrian, Clos, Jeremie and Wilson, Max L. (2019) Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks. In: Halfway to the Future Symposium 2019.

Full content URL: https://doi.org/10.1145/3363384.3363392

Documents
Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks
Published Open Access manuscript
[img]
[Download]
[img]
Preview
PDF
3363384.3363392.pdf - Whole Document
Available under License Creative Commons Attribution 4.0 International.

865kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper explores different approaches and configurations for classifying Mental Workload (MWL) from a continuous HCI task, to identify and understand potential limitations and data processing decisions. In particular, we investigate three overall approaches: a logistic regression method, a supervised shallow method (SVM), and a supervised deep learning method (CNN). We examine personalised and gen-eralised models, as well as consider different features and ways of labelling the data. Our initial explorations show that generalised models can perform as well as personalised ones and that deep learning can be a suitable approach for medium size datasets. To provide additional practical advice for future brain-computer interaction systems, we conclude by discussing the limitations and data-preparation needs of different machine learning approaches. We also make recommendations for avenues of future work that are most promising for the machine learning of fNIRS data.

Additional Information:Johann Benerradi, Horia A. Maior, Adrian Marinescu, Jeremie Clos, and Max L. Wilson. 2019. Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks. In Proceedings of the Halfway to the Future Symposium 2019 (HTTF 2019). Association for Computing Machinery, New York, NY, USA, Article 8, 1–11. DOI:https://doi.org/10.1145/3363384.3363392
Keywords:fNIRS, Deep Learning, Machine Learning
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:39603
Deposited On:20 Mar 2020 14:57

Repository Staff Only: item control page