Enterprise AI Analysis
Model-driven validation of visual explanations for multimodal emotion recognition
This study introduces a novel framework for validating AI-derived explanations in multimodal emotion recognition. By integrating brain-heart interplay (BHI) features with a CNN model, the system achieves remarkable accuracy (97-98%). A key innovation is the dynamic selection framework that evaluates explanation quality using fidelity and sensitivity metrics, autonomously identifying the most trustworthy explanation method. Integrated Gradients consistently outperformed other state-of-the-art XAI approaches in capturing global explanations, offering valuable neurophysiological insights into emotion processing.
Executive Impact: Key Performance Indicators
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Emotion Recognition Accuracy
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Performance Improvement (over EEG-only)
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Number of Emotional States Classified
Deep Analysis & Enterprise Applications
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The study proposes a novel framework for validating AI-derived explanations in emotion recognition. It leverages a CNN to process BHI features (derived from EEG and HRV data and rearranged as images). A model-agnostic methodology extracts local explanations, which are then dynamically evaluated for accuracy in representing specific emotional states using fidelity and sensitivity metrics. Integrated Gradients, DeepLIFT, Expected Gradients, and Grad-CAM were used as local explainers. The framework dynamically selects the optimal global explainer based on a combined optimization of fidelity and sensitivity.
Enterprise Process Flow
EEG & HRV Data Input
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BHI Feature Extraction
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Feature Reorganization as Images
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CNN Model Training (Emotion Recognition)
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Local Explanation Generation (IG, EG, DLIFT, GradCAM)
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Fidelity & Sensitivity Assessment
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Optimal Global Explanation Selection
Integrated Gradients
Consistently outperformed other XAI methods for global explanations.
The system achieved remarkable accuracy levels, consistently reaching approximately 97–98% across various emotion classification tasks, including 9-level arousal and valence, and nine discrete emotions. BHI features significantly outperformed EEG-PSD-based analysis, showing a 20% accuracy improvement. Specifically, HF-to-Brain features consistently achieved the highest accuracy in arousal (96.92% ± 0.30%) and valence (96.79% ± 0.28%) classification on the MAHNOB-HCI dataset.
97-98%
Across all emotion classification tasks.
| Feature Set | Arousal Accuracy | Valence Accuracy | Categorical Emotions Accuracy |
|---|---|---|---|
| BHI (HF-to-Brain) | 96.92% ± 0.30% | 96.79% ± 0.28% | 96.98% ± 0.48% |
| EEG-PSD | 73.23% ± 0.91% | 74.69% ± 0.88% | 74.72% ± 0.87% |
The study provides valuable neurophysiological insights by identifying brain regions and frequency bands most influential in emotion recognition. Theta and alpha bands of EEG signals, particularly in combination with BHI, played a prominent role. The HF-to-Brain feature was associated with the best-performing schema, aligning with existing literature on how emotion perception influences cardiovascular dynamics. The attribution maps revealed specific scalp locations and frequency bands' importance, contributing to a more comprehensive understanding of brain-heart interplay in emotional states.
Theta & Alpha Bands
Most influential EEG frequency bands for BHI-related emotional states.
Understanding Brain-Heart Interplay
HF-to-Brain Feature Significance
The HF-to-Brain feature proved to be the most performant, underscoring its critical role in emotion recognition and aligning with prior research on cardiovascular dynamics influencing emotion perception. This offers a nuanced view beyond mere EEG signals, integrating the body's autonomic responses into emotional understanding.
Directionality and Frequency Bands
While specific directionality (brain-to-heart vs. heart-to-brain) and precise heartbeat frequency bands (LF, HF) did not show clear distinctions in performance, the overall contribution of BHI-related features, regardless of these specifics, was significant, achieving over 93% accuracy. This highlights the holistic importance of brain-heart communication in emotional processing.
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