diff --git a/09_eeg_use_case.ipynb b/09_eeg_use_case.ipynb
index af4418e483200080069fb301cdc9390c2f4e3c92..6b89cd5a0fa32a3ad27e81ffc9b18951496570be 100644
--- a/09_eeg_use_case.ipynb
+++ b/09_eeg_use_case.ipynb
@@ -472,7 +472,9 @@
     "</div>\n",
     "\n",
     "<div class=\"alert alert-block alert-warning\">\n",
-    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>One-vs-rest classification</strong>  \n",
+    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>One-vs-rest classification</strong>\n",
+    "    <p> Multiclass classification can also be tranferred to multiple binary classification problems. One strategy is called One-vs-rest, where one classifier is trained per class. In our case this means that for each arm movement one classifier is trained by considering only the labels of the respective arm movement.\n",
+    "    </p>\n",
     "\n",
     "</div>"
    ]