# ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition

arXiv: https://arxiv.org/abs/2503.01750 · HTML: https://mlsanity.com/paper/2503.01750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [A Survey for Foundation Models in Autonomous Driving](https://mlsanity.com/paper/2402.01105/) (2024)
- [Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram](https://mlsanity.com/paper/2203.06889/) (2022)

## Abstract (arXiv)

Driver emotion recognition plays a crucial role in driver monitoring systems, enhancing human-autonomy interactions and the trustworthiness of Autonomous Driving (AD). Various physiological and behavioural modalities have been explored for this purpose, with Electrocardiogram (ECG) emerging as a standout choice for real-time emotion monitoring, particularly in dynamic and unpredictable driving conditions. Existing methods, however, often rely on multi-channel ECG signals recorded under static conditions, limiting their applicability in real-world dynamic driving scenarios. To address this limitation, the paper introduces ECG-EmotionNet, a novel architecture designed specifically for emotion recognition in dynamic driving environments. ECG-EmotionNet is constructed by adapting a recently introduced ECG Foundation Model (FM) and uniquely employs single-channel ECG signals, ensuring both robust generalizability and computational efficiency. Unlike conventional adaptation methods such as full fine-tuning, linear probing, or low-rank adaptation, we propose an intuitively pleasing alternative, referred to as the nested Mixture of Experts (MoE) adaptation. More precisely, each transformer layer of the underlying FM is treated as a separate expert, with embeddings extracted from these experts fused using trainable weights within a gating mechanism. This approach enhances the representation of both global and local ECG features, leading to a 6% improvement in accuracy and a 7% increase in the F1 score, all while maintaining computational efficiency. The effectiveness of the proposed ECG-EmotionNet architecture is evaluated using a recently introduced and challenging driver emotion monitoring dataset.