diff --git a/rec_to_nwb/processing/builder/originators/position_originator.py b/rec_to_nwb/processing/builder/originators/position_originator.py
index 5f3e4a08..bed43ca6 100644
--- a/rec_to_nwb/processing/builder/originators/position_originator.py
+++ b/rec_to_nwb/processing/builder/originators/position_originator.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 import pandas as pd
-from pynwb import NWBFile, ProcessingModule
+from pynwb import NWBFile, ProcessingModule, TimeSeries
 from pynwb.behavior import Position
 from rec_to_binaries.read_binaries import readTrodesExtractedDataFile
 from rec_to_nwb.processing.exceptions.invalid_metadata_exception import (
@@ -56,15 +56,63 @@ def make(self, nwb_content: NWBFile):
                 position_df = self.get_position_with_corrected_timestamps(
                     position_tracking_path[0], self.ptp_enabled
                 )
-                position.create_spatial_series(
-                    name=f"series_{dataset_ind}",
-                    description=", ".join(position_df.columns.tolist()),
-                    data=np.asarray(position_df),
-                    conversion=conversion,
-                    reference_frame="Upper left corner of video frame",
-                    timestamps=np.asarray(position_df.index),
-                )
+                # Multi-position split.
+                # TODO: generalize key names?
+                key_lists = [
+                    [
+                        "xloc",
+                        "yloc",
+                    ],  # led 0
+                    [
+                        "xloc2",
+                        "yloc2",
+                    ],
+                ]  # led 1
+                for led_number, valid_keys in enumerate(key_lists):
+                    key_set = [
+                        key for key in position_df.columns.tolist() if key in valid_keys
+                    ]
+                    if len(key_set) > 0:
+                        position.create_spatial_series(
+                            name=f"led_{led_number}_series_{dataset_ind}",
+                            description=", ".join(["xloc", "yloc"]),
+                            data=np.asarray(position_df[key_set]),
+                            conversion=conversion,
+                            reference_frame="Upper left corner of video frame",
+                            timestamps=np.asarray(position_df.index),
+                        )
                 first_timestamps.append(position_df.index[0])
+                # add the video frame index as a new processing module
+                if "position_frame_index" not in nwb_content.processing:
+                    nwb_content.create_processing_module(
+                        name="position_frame_index",
+                        description="stores video frame index for each position timestep",
+                    )
+                # add timeseries for each frame index set (once per series because led's share timestamps)
+                nwb_content.processing["position_frame_index"].add(
+                    TimeSeries(
+                        name=f"series_{dataset_ind}",
+                        data=np.asarray(position_df["video_frame_ind"]),
+                        unit="N/A",
+                        timestamps=np.asarray(position_df.index),
+                    )
+                )
+                # add the video non-repeat timestamp labels as a new processing module
+                if "non_repeat_timestamp_labels" not in nwb_content.processing:
+                    nwb_content.create_processing_module(
+                        name="non_repeat_timestamp_labels",
+                        description="stores non_repeat_labels for each position timestep",
+                    )
+                # add timeseries for each non-repeat timestamp labels set (once per series because led's share timestamps)
+                nwb_content.processing["non_repeat_timestamp_labels"].add(
+                    TimeSeries(
+                        name=f"series_{dataset_ind}",
+                        data=np.asarray(position_df["non_repeat_timestamp_labels"]),
+                        unit="N/A",
+                        timestamps=np.asarray(position_df.index),
+                    )
+                )
+
             except IndexError:
                 video_file_path = glob.glob(
                     os.path.join(pos_path, "*.pos_cameraHWFrameCount.dat")