Remove NeurIPS review-period framing; add real author block; link permanent GitHub repo

README.md

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 A longitudinal 64-channel EEG dataset and benchmark for studying focused attention meditation (FAM) and how its neural signatures evolve across a six-week training period. **74 healthy adults** were recorded at a pre-intervention baseline; **44** of them returned for a post-intervention follow-up. Three FAM techniques are systematically compared: **Hare Krishna mantra (HK)**, **SA-TA-NA-MA mantra (SA)**, and **Breath Focus (BF)**.
 
-The dataset accompanies the NeurIPS 2026 Datasets & Benchmarks Track submission *"L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark."* It defines three benchmark tasks: (1) cognitive-state decoding (rest vs. meditation), (2) fine-grained technique classification (HK / SA / BF), and (3) cross-session adaptation across the longitudinal gap.
+The dataset accompanies the paper *"L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark."* It defines three benchmark tasks: (1) cognitive-state decoding (rest vs. meditation), (2) fine-grained technique classification (HK / SA / BF), and (3) cross-session adaptation across the longitudinal gap.
 
 ## Highlights
 
@@ -121,7 +121,7 @@ Per-channel z-score normalisation is applied at run time by the dataset loader,
 | **Task 2** — Technique Classification | HK vs. SA vs. BF from `slMedita` (and/or `Medita`) | pre and post separately | 74 (pre) / 44 (post) | inter-subject 5-fold |
 | **Task 3** — Cross-Session Adaptation | Pre-trained Task-1 models applied to post-intervention `slMedita` vs. `restCE01` | post-intervention | 44 | zero-shot and N-shot (10/30) calibration; intra-subject |
 
-Reference benchmark code, configs, and baselines: <https://anonymous.4open.science/r/L-FAME-Benchmark> (anonymous repository hosted for the NeurIPS 2026 review period; will be replaced with a permanent GitHub URL upon acceptance).
+Reference benchmark code, configs, and baselines: <https://github.com/Angqi-Li/L-FAME-Benchmark>.
 
 ## Quick start
 
@@ -240,7 +240,7 @@ If you use this dataset or benchmark, please cite:
 
 ## Authors and acknowledgments
 
-> *Author and affiliation block redacted during the NeurIPS double-blind review period. Real names and affiliations will be added at acceptance.*
+**Authors:** Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri, Taosheng Liu, Barry H. Cohen, and Saiprasad Ravishankar.
 
 We thank **Devin O'Rourke** and **Sidharth Chhabra** at *The Harmony Collective* (Ypsilanti, MI) for expert guidance on the three meditation training protocols. We are grateful to the undergraduate and graduate student research assistants who collected and curated the EEG sessions: **Ab Basit Rafi Syed, Pratham Pradhan, Annie Wozniak, Vu Song Thuy Nguyen, Genevieve Orlewicz**, and **Alisia Coipel**. Most of all, we thank the 74 participants who completed the demanding longitudinal protocol.
 
@@ -251,6 +251,4 @@ We thank **Devin O'Rourke** and **Sidharth Chhabra** at *The Harmony Collective*
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 TODOs to address in follow-up commits (not blocking this README):
 1. Add a `ml_continuous_tensors` FileSet to croissant_metadata.json `distribution[]`.
-2. Replace anonymous.4open.science URL with permanent GitHub URL after NeurIPS acceptance.
-3. Replace author/affiliation/citation placeholders after acceptance.
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