DORAS
metadata only
VEOS: Vision-based Electrooculogram Signal Inference for Improved EEG in Brain-Computer Interfaces
Electroencephalography (EEG) is highly susceptible to ocular artefacts from eye movements and blinks, which contaminate frontal channels and degrade brain-computer interface (BCI) performance. Artefacts are commonly mitigated using electrooculog- raphy (EOG) as an auxiliary channel, but extra perioc
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
- Detección de convulsiones en señales EEG mediante técnicas de clasificación de segmentos y machine learning#1334873
- A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study#610017
- A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study#608955
- Definition of Links Between Non-invasives Methods and Intracerebral EEG With Simultaneous Recordings.#1476625
- A Hierarchical Visual EEG Framework for the Assessment of Disorders of Consciousness#339586
Record · ID 1499814
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