arXiv (All)
open access
A Multi-Objective Optimisation Framework for Corticomuscular EEG-EMG Pair Selection in Hybrid BCI
Hybrid brain-computer interface (BCI) systems that integrate electroencephalography (EEG) and electromyography (EMG) signals have shown significant potential in improving the reliability of motor imagery (MI) classification, particularly in neuro-rehabilitation applications. However, identifying inf
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
- Filter bank CSP with Riemannian weighting for disability-centric motor imagery brain computer interface.#150169
- Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition#1106098
- Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram#1115570
- Quantifying Event-Related (De)Synchronization Variability for Brain-Computer Interface: A Unified and Interpretable Framework#1129337
- Harnessing multimodal deep representation with dimensionality reducing approach for enhanced intrusion detection system in internet of things networks.#1144
Record · ID 1034043
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