Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces

• URL: http://arxiv.org/abs/2502.17445v1
• Date: Wed, 29 Jan 2025 14:31:57 GMT
• Title: Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces
• Authors: Xiaowei Jiang, Yanan Chen, Nikhil Ranjan Pal, Yu-Cheng Chang, Yunkai Yang, Thomas Do, Chin-Teng Lin,
• Abstract summary: We present iFuzzyAffectDuo, a novel model that integrates a dual-filter fuzzy neural network architecture for improved detection and interpretation of emotional states.<n>We validate our approach across three neuroimaging datasets using functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG)
• Score: 25.445687764717167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fuzzy logic provides a robust framework for enhancing explainability, particularly in domains requiring the interpretation of complex and ambiguous signals, such as brain-computer interface (BCI) systems. Despite significant advances in deep learning, interpreting human emotions remains a formidable challenge. In this work, we present iFuzzyAffectDuo, a novel computational model that integrates a dual-filter fuzzy neural network architecture for improved detection and interpretation of emotional states from neuroimaging data. The model introduces a new membership function (MF) based on the Laplace distribution, achieving superior accuracy and interpretability compared to traditional approaches. By refining the extraction of neural signals associated with specific emotions, iFuzzyAffectDuo offers a human-understandable framework that unravels the underlying decision-making processes. We validate our approach across three neuroimaging datasets using functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG), demonstrating its potential to advance affective computing. These findings open new pathways for understanding the neural basis of emotions and their application in enhancing human-computer interaction.

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