diff --git a/QtBrainChartGUI/core/muse_qc.py b/QtBrainChartGUI/core/muse_qc.py
new file mode 100644
index 000000000..90116359d
--- /dev/null
+++ b/QtBrainChartGUI/core/muse_qc.py
@@ -0,0 +1,83 @@
+import pandas as pd
+import numpy as np
+import sklearn.decomposition
+from sklearn.decomposition import PCA
+from sys import argv
+from sklearn.preprocessing import StandardScaler
+
+def getMHD(ROIs, ROIsReference, n_components=3):
+    
+    '''
+    Usage Information
+    
+    ROIs: pandas dataframe or numpy array with numeric values
+    
+    ROIsReference: PCA object (numpy array) with column number equals to n_components, or numpy array with numeric values (will calculate PCA)
+    
+    '''
+    
+    # check ROIs's type; if dataframe, convert to numpy array; if numpy array, continue; if else, exit
+    if isinstance(ROIs, pd.DataFrame):
+        ROIs = ROIs.to_numpy()
+    elif isinstance(ROIs, np.ndarray):
+        pass
+    else:
+        print("Please check input ROIs file type! Expecting pandas dataframe or numpy array!")
+        exit(1)
+        
+    # check if ROIs is numeric; will get error if can't be converted to numeric
+    ROIs = ROIs.astype(float)
+    
+    # check if the number of columns is larger than n_components
+    n_col = ROIs.shape[1]
+    if n_col <= n_components:
+        print("Please make sure the number of columns is larger than n_components !")
+        exit(1)
+        
+    # check if ROIsReference is a numpy array; if yes, then compute PCA object using ROIsRefenece as data and apply model on ROIs; if is a PCA object, apply model on ROIs
+    if isinstance(ROIsReference, np.ndarray): 
+        
+        if ROIsReference.shape[1] > n_components:
+            # standardize ROIsReference and fit the model
+            ROIsReference_S = StandardScaler().fit_transform(ROIsReference) 
+            pca = PCA(n_components = n_components)
+            pca_ref = pca.fit(ROIsReference_S)
+            # standardize ROIs and apply model 
+            ROIs_S = StandardScaler().fit_transform(ROIs)
+            ROIs_transformed = pca_ref.transform(ROIs_S)
+            
+        else:
+            print("Please check your ROIsReference!")
+            exit(1)
+            
+    elif isinstance(ROIsReference, sklearn.decomposition._pca.PCA):      
+        # standardize ROIs and apply model 
+        ROIs_S = StandardScaler().fit_transform(ROIs)
+        ROIs_transformed = ROIsReference.transform(ROIs_S)
+        
+    else:
+        print("Please check your ROIsReference file type!")
+        exit(1)
+        
+        
+    # calculateMahalanobis
+    import scipy as sp
+    from scipy.spatial.distance import mahalanobis
+    
+    # removing subjects with inf values
+    ROIs_transformed_red = ROIs_transformed[np.where((ROIs_transformed.mean(axis=1) != np.inf) & (ROIs_transformed.mean(axis=1) != np.nan))[0],:]
+
+    # calculate mean values of the roi values
+    ROIs_transformed_mean = ROIs_transformed_red.mean(axis=0 )
+ 
+    # calculate inverse of the covariance matrix
+    VI = sp.linalg.inv( np.cov(ROIs_transformed_red, rowvar=False))
+
+    # calculate squared mahalanobis distance
+    md = np.zeros(ROIs_transformed.shape[0])
+    for i in range(ROIs_transformed.shape[0]):
+        md[i] = mahalanobis( ROIs_transformed_mean, ROIs_transformed[i], VI) ** 2
+
+    # return values
+    return np.sqrt(md)
+