STAMP: Spatial-Temporal Adapter with Multi-Head Pooling
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913095225442304 |
|---|---|
| author | Shook, Brad Turner, Abby Chen, Jieshi Wiliński, Michał Goswami, Mononito Elmer, Jonathan Dubrawski, Artur |
| author_facet | Shook, Brad Turner, Abby Chen, Jieshi Wiliński, Michał Goswami, Mononito Elmer, Jonathan Dubrawski, Artur |
| contents | Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop foundation models specific to electroencephalography (EEG) data, which records brain electrical activity as time series. However, no comparative analysis of EEG-specific foundation models (EEGFMs) versus general TSFMs has been performed on EEG-specific tasks. We introduce a novel Spatial-Temporal Adapter with Multi-Head Pooling (STAMP), which leverages univariate embeddings produced by a general TSFM, implicitly models spatial-temporal characteristics of EEG data, and achieves performance comparable to state-of-the-art EEGFMs. A comprehensive analysis is performed on 8 benchmark datasets of clinical tasks using EEG for classification, along with ablation studies. Our proposed adapter is lightweight in trainable parameters and flexible in the inputs it can accommodate, supporting easy modeling of EEG data using TSFMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STAMP: Spatial-Temporal Adapter with Multi-Head Pooling Shook, Brad Turner, Abby Chen, Jieshi Wiliński, Michał Goswami, Mononito Elmer, Jonathan Dubrawski, Artur Machine Learning Artificial Intelligence Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop foundation models specific to electroencephalography (EEG) data, which records brain electrical activity as time series. However, no comparative analysis of EEG-specific foundation models (EEGFMs) versus general TSFMs has been performed on EEG-specific tasks. We introduce a novel Spatial-Temporal Adapter with Multi-Head Pooling (STAMP), which leverages univariate embeddings produced by a general TSFM, implicitly models spatial-temporal characteristics of EEG data, and achieves performance comparable to state-of-the-art EEGFMs. A comprehensive analysis is performed on 8 benchmark datasets of clinical tasks using EEG for classification, along with ablation studies. Our proposed adapter is lightweight in trainable parameters and flexible in the inputs it can accommodate, supporting easy modeling of EEG data using TSFMs. |
| title | STAMP: Spatial-Temporal Adapter with Multi-Head Pooling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.10848 |