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

arXiv: https://arxiv.org/abs/2502.17445 · HTML: https://mlsanity.com/paper/2502.17445/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [4D Attention-based Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2101.05484/) (2021)
- [A Fuzzy-based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy](https://mlsanity.com/paper/2409.17661/) (2024)
- [iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System](https://mlsanity.com/paper/2410.12267/) (2024)

## Built on by (most cited first, 2 of 2)

- [Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts](https://mlsanity.com/paper/2604.24765/) (2026)
- [iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration](https://mlsanity.com/paper/2608.14646/) (2026)

## Abstract (arXiv)

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.