Mental Workload Detection and Classification Using fNIRS and Machine Learning

  • Zürich
  • Zurich

Website University of Zurich & University Hospital Zurich

Background Mental Workload:

Mental workload, also known as cognitive workload, refers to the mental effort and resources required to perform a specific task or activity. It encompasses various cognitive processes such as attention, memory, problem-solving, decision-making, and perception. The more demanding a task, the higher the mental workload required to perform it effectively. In the context of Human-Computer Interaction (HCI), mental workload is critical as it affects the user’s ability to process information efficiently. When the workload exceeds a user’s cognitive capacity, information processing slows down, leading to increased errors and reduced performance. Understanding and measuring mental workload is crucial because it affects our productivity, mental health, and overall well-being.

 

Background Stroke:

Stroke is a major cause of motor impairments, leading to an increased demand for effective rehabilitation methods. Technological solutions like VR-based rehabilitation and robotic movement training systems show promise in minimally supervised settings. However, maintaining patient engagement is a challenge. Balancing visual, memory, and attentional load is critical, especially for stroke patients sensitive to excessive task load. This project aims to develop an adaptive neurorehabilitation system using fNIRS to measure and adjust mental workload, enhancing patient engagement and recovery.

Keywords: Mental workload; fNIRS; real-time; machine learning

Description

This project will focus on measuring different types of mental workload using functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. Students can choose one of the following topics to deeply investigate: working memory load, attentional load, or visual perceptual load.

Student Project Phases:

1. Research Phase: Understand principles of fNIRS signals and mental workload.

2. Experiment Design and Implementation: Develop/modify MATLAB code for behavioral experiments and fNIRS data collection. Topics include:

  • Working Memory Load: Implement tasks with varying memory loads.
  • Visual Perception Load: Create visual stimuli tasks to measure perception load.
  • Attentional Load: Implement tasks varying attentional demands.

3. Data Collection: Conduct experiments to gather behavioral and fNIRS data.

4. Algorithm Development: Implement machine learning algorithms to analyze data and train classifiers for mental workload.

5. Testing and Evaluation: Validate classifiers to ensure reliability and accuracy.

6.Documentation: Document progress, experimental designs, data collection, and machine learning implementations.

Required Skills: 

  • Basic knowledge in MATLAB and Python
  • Understanding of signal processing techniques and statistics
  • Basic knowledge of machine learning
  • Good communication skills for documenting progress and presenting findings

Goal:

  • Implement and optimize experiments to measure mental workload using fNIRS.
  • Develop machine learning classifiers to classify different levels of mental workload.

Contact Details:

This project will be supervised by Jiahui An (jiahui.an@uzh.ch) and Dr. Josef Scheonhammer (josef.schoenhammer@uzh.ch)

The study will be conducted at: NeuroCoRe Lab (https://www.stroke-neurocore.uzh.ch/en.html) & Stroke Center, University Hospital Zurich & University of Zurich

To apply for this job email your details to jiahui.an@uzh.ch