arXiv (OAI Expanded)
open access
ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information
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
- ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding#1119040
- Band-Gated Identity-Disentangled Training for Cross-Subject Auditory Attention Decoding#970618
- Learning aligned EEG representations with subject-specific encoders#1053466
- Learning aligned EEG representations with subject-specific encoders#1122472
- EEG Based BCI for Upper Limb Rehabilitation in Stroke#810847
Record · ID 1417946
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