# Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals

arXiv: https://arxiv.org/abs/2310.07648 · HTML: https://mlsanity.com/paper/2310.07648/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2023.

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

- [Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters](https://mlsanity.com/paper/2102.08597/) (2021)
- [Quaternion Convolutional Neural Networks for Heterogeneous Image Processing](https://mlsanity.com/paper/1811.02656/) (2018)
- [PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions](https://mlsanity.com/paper/2110.04176/) (2021)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [Deep Transfer Learning for EEG-based Brain Computer Interface](https://mlsanity.com/paper/1808.01752/) (2018)

## Built on by (most cited first, 8 of 8)

- [Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models](https://mlsanity.com/paper/2410.02780/) (2024)
- [Hierarchical Hypercomplex Network for Multimodal Emotion Recognition](https://mlsanity.com/paper/2409.09194/) (2024)
- [Towards Explaining Hypercomplex Neural Networks](https://mlsanity.com/paper/2403.17929/) (2024)
- [PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2504.17163/) (2025)
- [PHemoNet: A Multimodal Network for Physiological Signals](https://mlsanity.com/paper/2410.00010/) (2024)
- [Cross-Temporal Attention Fusion (CTAF) for Multimodal Physiological Signals in Self-Supervised Learning](https://mlsanity.com/paper/2602.02784/) (2026)
- [Multimodal Functional Maximum Correlation for Emotion Recognition](https://mlsanity.com/paper/2512.23076/) (2025)
- [Multimodal Wearable-Based Olfactory-Induced Emotion Recognition in Arousal-Valence Dimensions](https://mlsanity.com/paper/2608.00043/) (2026)

## Abstract (arXiv)

Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information. Existing deep learning-based methods still rely on extracted handcrafted features, not taking full advantage of the learning ability of neural networks, and often adopt a single-modality approach, while human emotions are inherently expressed in a multimodal way. In this paper, we propose a hypercomplex multimodal network equipped with a novel fusion module comprising parameterized hypercomplex multiplications. Indeed, by operating in a hypercomplex domain the operations follow algebraic rules which allow to model latent relations among learned feature dimensions for a more effective fusion step. We perform classification of valence and arousal from electroencephalogram (EEG) and peripheral physiological signals, employing the publicly available database MAHNOB-HCI surpassing a multimodal state-of-the-art network. The code of our work is freely available at https://github.com/ispamm/MHyEEG.