The Nencki-Symfonia EEG/ERP dataset
ds004621 · 17 high-confidence citations
- The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults
- An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior
- Common and distinct <scp>BOLD</scp> correlates of Simon and flanker conflicts which can(not) be reduced to time‐on‐task effects
- BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories
- Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models
- Multi-Source Neural Activity Indices for EEG/MEG Localization: A Two-Stage Spatial Filtering Framework and Extension to MNE-Python
- Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements
- Common but different: An ERP study of single- and multi-source interference processing in MSIT
- Cortical oscillations reflect opponent ensemble dynamics through coordinated multifrequency activity
- Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model
- The individual alpha bandwidth: A trait-like neural marker associated with sensory processing and executive function
- Gemini Exchange v2.0.0: Empirical Validation of Dynamic Information–Energy Coupling in Cortical EEG Activity
- ACL Framework: Real EEG Validation of Semantic Operators (∘, ⊗, ↻) — Derived Metrics and Reproducible Analysis Package
- ACL Framework: Real EEG Validation of Semantic Operators (∘, ⊗, ↻) — Derived Metrics and Reproducible Analysis Package
- The Anti-Congruent Law (Version 3): Empirical Validation of ψ-Scaling in Human Semantic EEG Dynamics
- The Anti-Congruent Law (ACL) — Boundary Test v3.1.0: Empirical Falsification of ψ⁻⁴ Scaling in OpenNeuro EEG
- Gemini Exchange v2.0.0: Empirical Validation of Dynamic Information–Energy Coupling in Cortical EEG Activity
No citations of that kind for this dataset.