diff --git a/3_Cognitive_BCI/Sleep_Inertia_Analysis_Using_EEG_data/EEG_connectivity_map_by_region.py b/3_Cognitive_BCI/Sleep_Inertia_Analysis_Using_EEG_data/EEG_connectivity_map_by_region.py
new file mode 100644
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+++ b/3_Cognitive_BCI/Sleep_Inertia_Analysis_Using_EEG_data/EEG_connectivity_map_by_region.py
@@ -0,0 +1,144 @@
+# Code modify by hn_jo@korea.ac.kr 
+# last update 2023.11.27
+# Input : raw data
+# Output : connectivity map by region (N by N matrix, N is number of region)
+
+import numpy as np
+import mne_connectivity
+
+def get_connectivity(data, method):
+    con = mne_connectivity.spectral_connectivity_epochs(
+        data,
+        method=method,
+        mode="multitaper",
+        sfreq=sfreq,
+        fmin=fmin,
+        fmax=fmax,
+        faverage=True,
+        tmin=tmin,
+        mt_adaptive=False,
+        n_jobs=1,
+    )
+    return con
+
+
+def connectivity_mean(con):
+    con_mean = []
+    con_mean = con[0].get_data("dense")[:, :, 0].copy()
+    for sub in range(1, len(con)):
+        con_mean = np.add(con_mean, con[sub].get_data("dense")[:, :, 0].copy())
+    con_mean = np.divide(con_mean, len(con))
+    return con_mean
+
+
+def ch_to_idx(ch_names, ch_list):
+    ch_idx = []
+    for ch in ch_list:
+        ch_idx.append(ch_names.index(ch))
+    return ch_idx
+
+
+def get_roi_con(con, region1, region2):
+    roi_con = []
+    for i in region1:
+        for j in region2:
+            if i != j:
+                if i < j:
+                    roi_con.append(con[j][i])
+                else:
+                    roi_con.append(con[i][j])
+    return np.mean(roi_con)
+
+
+def get_roi_map(con, ch):
+    roi_map = np.zeros((5, 5))
+    roi_list = [frontal, central, temporal, parietal, occipital]
+    for i in range(0, 5):
+        for j in range(0, 5):
+            roi_map[i][j] = get_roi_con(
+                con, ch_to_idx(ch, roi_list[i]), ch_to_idx(ch, roi_list[j])
+            )
+    return roi_map
+
+
+# example 
+# This example based on BrainVision data
+# You can modify this code for your own data
+
+sfreq = 100 # sampling frequency
+fmin = 8 # frequency min
+fmax = 12 # frequency max
+tmin = 0 # time min
+
+ground = "Fpz"
+reference = "Cz"
+frontal = [
+    "Fp1",
+    "Fp2",
+    "AFz",
+    "AF3",
+    "AF4",
+    "AF7",
+    "AF8",
+    "Fz",
+    "F1",
+    "F2",
+    "F3",
+    "F4",
+    "F5",
+    "F6",
+    "F7",
+    "F8",
+]
+central = [
+    "FC1",
+    "FC2",
+    "FC3",
+    "FC4",
+    "FC5",
+    "FC6",
+    "Cz",
+    "C1",
+    "C2",
+    "C3",
+    "C4",
+    "C5",
+    "C6",
+]
+temporal = ["FT7", "FT8", "FT9", "FT10", "T7", "T8", "TP7", "TP8", "TP9", "TP10"]
+parietal = [
+    "CPz",
+    "CP1",
+    "CP2",
+    "CP3",
+    "CP4",
+    "CP5",
+    "CP6",
+    "Pz",
+    "P1",
+    "P2",
+    "P3",
+    "P4",
+    "P5",
+    "P6",
+    "P7",
+    "P8",
+]
+occipital = ["POz", "PO3", "PO4", "PO7", "PO8", "Oz", "O1", "O2", "Iz"]
+
+baseline = [] # baseline connectivity data
+g1 = [] # group 1 connectivity data
+g2 = [] # group 2 connectivity data
+g3 = [] # group 3 connectivity data
+group_list = [baseline, g1, g2, g3]
+data_list = [baseline_data, g1_data, g2_data, g3_data] # raw data list
+method = "pli"
+
+for idx, con_value in enumerate(group_list):
+    for sub in range(len(data_list[idx])):
+        con_value.append(get_connectivity(data_list[idx][sub], method))
+
+for idx, value in enumerate(group_list):
+    mean = []
+    mean = connectivity_mean(value)
+    roi = get_roi_map(mean, ch_names) # ch_names is channel name list from raw data