# Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2512.07820 · HTML: https://mlsanity.com/paper/2512.07820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Balanced Multimodal Learning via On-the-fly Gradient Modulation](https://mlsanity.com/paper/2203.15332/) (2022)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance](https://mlsanity.com/paper/2405.17730/) (2024)
- [A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition](https://mlsanity.com/paper/1906.01704/) (2019)
- [EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces](https://mlsanity.com/paper/2405.00719/) (2024)
- [PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition](https://mlsanity.com/paper/2302.02520/) (2023)
- [Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines](https://mlsanity.com/paper/2304.04273/) (2023)
- [AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition](https://mlsanity.com/paper/2212.12134/) (2022)
- [CLARE: Cognitive Load Assessment in REaltime with Multimodal Data](https://mlsanity.com/paper/2404.17098/) (2024)
- [Git Loss for Deep Face Recognition](https://mlsanity.com/paper/1807.08512/) (2018)
- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations](https://mlsanity.com/paper/2405.20082/) (2024)

## Abstract (arXiv)

We present a novel graph-based learning of EEG representations with gradient alignment (GEEGA) that leverages multi-domain information to learn EEG representations for brain-computer interfaces. Our model leverages graph convolutional networks to fuse embeddings from frequency-based topographical maps and time-frequency spectrograms, capturing inter-domain relationships. GEEGA addresses the challenge of achieving high inter-class separability, which arises from the temporally dynamic and subject-sensitive nature of EEG signals by incorporating the center loss and pairwise difference loss. Additionally, GEEGA incorporates a gradient alignment strategy to resolve conflicts between gradients from different domains and the fused embeddings, ensuring that discrepancies, where gradients point in conflicting directions, are aligned toward a unified optimization direction. We validate the efficacy of our method through extensive experiments on three publicly available EEG datasets: BCI-2a, CL-Drive and CLARE. Comprehensive ablation studies further highlight the impact of various components of our model.