Figshare
open access
CC BY 4.0
Subject-level dyslexia detection via optimized EEG channel selection and compact CNN analysis of multi-window spectrograms
artificial-intelligencebiomedical-and-clinical-sciencesbiomedical-engineeringengineeringhealth-scienceshealth-services-and-systemsinformation-and-computing-sciencesneurosciences
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
- Minimal evidence of adaptation deficits in children with dyslexia: An EEG study with controlled expectations#16340
- Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls#405661
- Can object identification difficulty be predicted based on disfluencies and eye-movements in connected speech?#685074
- TOWARDS INTELLIGENT AND EFFICIENT EEG-BASED BRAIN COMPUTER INTERFACES VIA AI-DRIVEN NEURAL DECODING AND CONTEXT-AWARE COMMUNICATION#637461
- Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.#320612
Record · ID 268299
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