# Neural decoding from stereotactic EEG: accounting for electrode variability across subjects arXiv: https://arxiv.org/abs/2411.10458 ยท HTML: https://mlsanity.com/paper/2411.10458/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 48 references that are in the map. - First posted on arXiv 1 Nov 2024. ## Builds on (most cited first, 14 of 14) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021) - [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018) - [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020) - [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015) - [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023) - [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023) - [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021) - [A Modulation Module for Multi-task Learning with Applications in Image Retrieval](https://mlsanity.com/paper/1807.06708/) (2018) - [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017) - [LFADS - Latent Factor Analysis via Dynamical Systems](https://mlsanity.com/paper/1608.06315/) (2016) - [STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer](https://mlsanity.com/paper/2206.04727/) (2022) - [Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2208.00877/) (2022) ## Built on by (most cited first, 17 of 17) - [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025) - [Neural Codecs as Biosignal Tokenizers](https://mlsanity.com/paper/2510.09095/) (2025) - [BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity](https://mlsanity.com/paper/2512.12135/) (2025) - [Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling](https://mlsanity.com/paper/2512.12461/) (2025) - [Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods](https://mlsanity.com/paper/2604.27033/) (2026) - [BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics](https://mlsanity.com/paper/2505.20480/) (2025) - [Functional embeddings enable Aggregation of multi-area SEEG recordings over subjects and sessions](https://mlsanity.com/paper/2510.27090/) (2025) - [Cross-Subject Modeling for Widefield Calcium Imaging via Atlas-Aligned Spatiotemporal Tokenization](https://mlsanity.com/paper/2607.09754/) (2026) - [Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants](https://mlsanity.com/paper/2505.21304/) (2025) - [A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations](https://mlsanity.com/paper/2506.13835/) (2025) - [PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding](https://mlsanity.com/paper/2508.11357/) (2025) - [A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2510.20683/) (2025) - [NeuroSketch: A Practical Design Recipe for Neural Decoding](https://mlsanity.com/paper/2512.09524/) (2025) - [BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation](https://mlsanity.com/paper/2601.17625/) (2026) - [MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models](https://mlsanity.com/paper/2605.24679/) (2026) - [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026) - [Pretraining for Sample-Efficient Neural Interfaces](https://mlsanity.com/paper/2609.13507/) (2026) ## Abstract (arXiv) Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial. However, in sEEG cohorts, each subject has a variable number of electrodes placed at distinct locations in their brain, solely based on clinical needs. Such heterogeneity in electrode number/placement poses a significant challenge for data integration, since there is no clear correspondence of the neural activity recorded at distinct sites between individuals. Here we introduce seegnificant: a training framework and architecture that can be used to decode behavior across subjects using sEEG data. We tokenize the neural activity within electrodes using convolutions and extract long-term temporal dependencies between tokens using self-attention in the time dimension. The 3D location of each electrode is then mixed with the tokens, followed by another self-attention in the electrode dimension to extract effective spatiotemporal neural representations. Subject-specific heads are then used for downstream decoding tasks. Using this approach, we construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task. We demonstrate that our model is able to decode the trial-wise response time of the subjects during the behavioral task solely from neural data. We also show that the neural representations learned by pretraining our model across individuals can be transferred in a few-shot manner to new subjects. This work introduces a scalable approach towards sEEG data integration for multi-subject model training, paving the way for cross-subject generalization for sEEG decoding.