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Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning
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DOI：10.3390/s23115147.png)
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摘要
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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.
Keyword:
usability testing
emotion detection
Brain-Computer Interface
channel selection
EEG signal processing
deep-learning
recurrent neural network
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification.png)
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