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Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

Sofien Gannouni1, Kais Belwafi1,2, Arwa Aledaily1

  • 1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

Detecting human emotions with electroencephalography (EEG) brain signals offers valuable insights for usability testing. This study introduces a novel EEG-based framework achieving over 92% accuracy in emotion recognition for software development.

Keywords:
Brain-Computer InterfaceEEG signal processingchannel selectiondeep-learningemotion detectionrecurrent neural networkusability testing

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Electroencephalography (EEG) is a cost-effective technology for measuring brain activity.
  • Detecting human emotions via EEG signals is gaining traction for applications like usability testing.
  • Understanding user emotions can significantly impact software production and user satisfaction.

Purpose of the Study:

  • To propose an original framework for usability testing utilizing EEG-based emotion detection.
  • To provide an accurate and precise method for understanding user satisfaction in software development.
  • To enhance the development process and user experience through objective emotional feedback.

Main Methods:

  • Utilized a recurrent neural network (RNN) as a classification algorithm.
  • Employed feature extraction techniques based on event-related desynchronization (ERD) and event-related synchronization (ERS) analysis.
  • Introduced a novel adaptive method for selecting EEG sources for emotion recognition.

Main Results:

  • The proposed framework achieved high accuracy rates in emotion dimension recognition.
  • Valence dimension accuracy: 92.13%.
  • Arousal and dominance dimension accuracy: 92.67% and 92.24%, respectively.

Conclusions:

  • The developed EEG-based framework demonstrates significant promise for emotion detection in usability testing.
  • This approach offers a valuable tool for in-depth and accurate assessment of user satisfaction.
  • The findings suggest a potential to revolutionize software development by integrating objective emotional feedback.