ArticleSensors (Basel, Switzerland)2023
Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Subject Identity Confounds qEEG Emotion Recognition on DEAP and DREAMER.Sensors (Basel, Switzerland) · 2026Article
- Electroencephalogram-Based Analysis of Monomodal and Multimodal Interaction in Mixed Reality Games.Sensors (Basel, Switzerland) · 2026Article
- Emotion Recognition Based on Fusion of Topological Features and Trajectory Images Derived from EEG Phase Space Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- Product concept design method for improving user satisfaction based on case-based reasoning.Scientific reports · 2025Article
- Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.Brain sciences · 2024Article
- Virtual reality-enabled high-performance emotion estimation with the most significant channel pairs.Heliyon · 2024Article
- Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition.Heliyon · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usability testing based on emotion detection using EEG signals, which can significantly affect software production and user satisfaction. This approach can provide an in-depth understanding of user satisfaction accurately and precisely, making it a valuable tool in software development. The proposed framework includes a recurrent neural network algorithm as a classifier, a feature extraction algorithm based on event-related desynchronization and event-related synchronization analysis, and a new method for selecting EEG sources adaptively for emotion recognition. The framework results are promising, achieving 92.13%, 92.67%, and 92.24% for the valence-arousal-dominance dimensions, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.