Update README.md

README.md

@@ -127,7 +127,7 @@ model-index:
         metrics:
           - type: r2
             value: 0.116
-            name: R squared
+            name: R-squared
           - type: rmse
             value: 0.1482
             name: Root Mean Squared Error
@@ -144,6 +144,22 @@ model-index:
           - type: cohen_kappa
             value: 0.191
             name: Cohen's Kappa
+      - task:
+          type: time-series-classification
+          name: Major Depressive Disorder Detection
+        dataset:
+          type: MODMA
+          name: MODMA
+        metrics:
+          - type: balanced_accuracy
+            value: 0.595
+            name: Balanced Accuracy (%)
+          - type: roc_auc
+            value: 0.448
+            name: AUROC
+          - type: pr_auc
+            value: 0.420
+            name: AUC-PR
 ---
 <div align="center">
   <img src="https://raw.githubusercontent.com/pulp-bio/BioFoundation/refs/heads/main/docs/model/logo/LuMamba_logo.png" alt="LuMamba Logo" width="800"/>
@@ -214,12 +230,19 @@ Larger model sizes can be attained by increasing the number of bi-Mamba blocks `
 
 ---
 
-## 📊 Results (Highlights)
+## 📊 Results
 
-- **TUAB (abnormal vs normal):** 80.99 % Bal. Acc., 0.883 AUROC, 0.892 AUPR.
- (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
+- **TUAB (abnormal vs normal):** 80.99 % Bal. Acc., 0.883 AUROC, 0.892 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
+- **TUSL (slowing event VS. seizure detection)**: 0.708 AUROC, 0.289 AUPR (LuMamba-Tiny, pre-trained with reconstruction-only).
+- **TUAR (artifact detection)**: 0.914 AUROC, 0.510 AUPR (LuMamba-Tiny, pre-trained with reconstruction-only).
 - **APAVA (Alzheimer's detection)**: 0.955 AUROC, 0.970 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
 - **TDBrain (Parkinson's detection)**: 0.961 AUROC, 0.960 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
+- **Mumtaz2016 (Depression detection)**: 0.725 Bal. Acc., 0.931 AUROC, 0.952 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
+- **SEED-V (5-class emotion detection)**: 0.350 Bal. Acc., 0.191 Cohen's Kappa (LuMamba-Tiny, pre-trained with reconstruction-only).
+- **MoBI (gait prediction)**: 0.116 R-squared, 0.148 RMSE (LuMamba-Tiny, pre-trained with reconstruction-only).
+- **MODMA (full 128-channel set)**: 59.47 % Bal. Acc., 0.448 AUROC, 0.420 AUPR (LuMamba-Tiny, pre-trained with reconstruction-only)
+- **MODMA (reduced 13-channel subset)**: 59.09 % Bal. Acc., 0.522 AUROC, 0.4153 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
+
 
 **Efficiency:** Up to **377× fewer FLOPs** relative to transformer-based baselines and supporting up to **500x longer** EEG windows, thanks to the efficient FEMBA bi-Mamba encoder.
 
@@ -336,7 +359,7 @@ If you use LuMamba, please cite:
 - **[LUNA](https://huggingface.co/PulpBio/LUNA)** — Transformer-based topology-agnostic EEG foundation model (NeurIPS 2025). Source of the channel-unification cross-attention module that LuMamba reuses.
 - **[FEMBA](https://huggingface.co/PulpBio/FEMBA)** — Bidirectional Mamba foundation model for EEG. Source of the linear-complexity temporal backbone that LuMamba reuses.
 - **[TinyMyo](https://huggingface.co/PulpBio/TinyMyo)** — Tiny foundation model for flexible EMG signal processing at the edge.
-- 
+  
 ## 🗒️ Changelog
 
 - **v1.0:** Initial release of LuMamba model card with task-specific checkpoints and instructions.