# NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection

arXiv: https://arxiv.org/abs/2610.20349 · HTML: https://mlsanity.com/paper/2610.20349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 19 of 19)

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- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [Tokenizing Single-Channel EEG with Time-Frequency Motif Learning](https://mlsanity.com/paper/2502.16060/) (2025)
- [DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal](https://mlsanity.com/paper/1812.04079/) (2018)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications](https://mlsanity.com/paper/2206.08672/) (2022)
- [Avoiding Post-Processing with Event-Based Detection in Biomedical Signals](https://mlsanity.com/paper/2209.11007/) (2022)
- [Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09006/) (2024)
- [Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition](https://mlsanity.com/paper/2310.14165/) (2023)

## Abstract (arXiv)

Asynchronous EEG event detection supports practical brain-computer interfaces by identifying event classes and temporal boundaries directly from continuous recordings without predefined event boundaries at inference time. The primary challenge lies in extracting event-related patterns embedded in ongoing background activity. To address this, we propose NeuroDETR, a discrete spatiotemporal multiscale framework that combines structured tokenization with event-aware feature refinement. Multiscale temporal codebooks capture brief responses and sustained dynamics, while Spatial-Pyramid VQ hierarchically encodes spatial structure across EEG channels. These complementary representations form a compact event memory for jointly classifying events and estimating their onsets and offsets. Before tokenization, we apply FOCAL, an event-aware sparse attention module that uses predicted event presence to guide temporal feature refinement and spatial aggregation, emphasizing likely event periods while preserving context across the recording. Experiments on eight EEG datasets that span diverse paradigms demonstrate state-of-the-art performance. These findings provide a basis for the development of BCI systems that recognize meaningful neural activity during continuous interaction and monitoring.