Upload DBbun_EEG_Validation.ipynb

DBbun_EEG_Validation.ipynb

@@ -863,30 +863,6 @@
     "for p in pngs:\n",
     "    display(Image(filename=str(p)))\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0abd4e7b",
-   "metadata": {},
-   "source": [
-    "# 🧩 Interpreting the Results\n",
-    "\n",
-    "- **Reconstruction loss (L1 or Huber):**  \n",
-    "  Lower values mean the autoencoder is accurately rebuilding EEG windows.  \n",
-    "  A mean loss below **≈0.2** indicates that the encoder captured the key EEG structure well.  \n",
-    "  Losses above **≈0.5** may signal noise, poor normalization, or early training.\n",
-    "\n",
-    "- **Embedding plots (PCA or UMAP):**  \n",
-    "  Clusters that separate by patient, seizure state, or signal pattern suggest meaningful latent structure.  \n",
-    "  Overlapping or random scatter implies the encoder still lacks discriminative power.\n",
-    "\n",
-    "- **Example reconstructions:**  \n",
-    "  In plots where blue is the original and orange is the reconstruction:  \n",
-    "  - Good alignment → model captures temporal dynamics.  \n",
-    "  - Phase shifts or amplitude mismatches → missing fine-grained details.\n",
-    "\n",
-    "Overall, losses around 0.2–0.3 with visibly similar reconstructions are **good** indicators that pretraining succeeded.\n"
-   ]
   }
  ],
  "metadata": {