Europe PMC
open access
EEG–ShuffleFormer: A Multi-View Hybrid Network Integrating Time–Frequency and Raw Signal Representations for Few-Channel Motor Imagery EEG Classification
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging.#1158638
- ADHD classification using spherical phase space partitioning and symbolic time series analysis of multi-channel EEG signals.#1091169
- Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets#155288
- Multimodal Pretraining for Generalizable EEG Representation Learning#394463
- Seizure detection using hierarchical temporal trend integration and self-supervised learning.#469310
Record · ID 231265
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive