diff --git a/halfpipe2bids/tests/test_utils.py b/halfpipe2bids/tests/test_utils.py
new file mode 100644
index 0000000..957b33f
--- /dev/null
+++ b/halfpipe2bids/tests/test_utils.py
@@ -0,0 +1,34 @@
+from halfpipe2bids.utils import regex_to_regressor
+
+
+def test_regex_to_regressor():
+    regex_confounds = [
+        "c_comp_cor_0[0-4]",
+        "(trans|rot)_[xyz]",
+        "global_signal",
+        "motion_outlier[0-9]+",
+    ]
+    confounds_columns = [
+        "c_comp_cor_00",
+        "c_comp_cor_01",
+        "a_comp_cor_00",
+        "global_signal",
+        "global_signal_derivative1",
+        "white_matter",
+        "trans_x",
+        "trans_x_derivative1",
+        "rot_y",
+        "rot_x_derivative1",
+        "motion_outlier1",
+        "motion_outlier2",
+    ]
+    matched = regex_to_regressor(regex_confounds, confounds_columns)
+    assert matched == [
+        "c_comp_cor_00",
+        "c_comp_cor_01",
+        "global_signal",
+        "trans_x",
+        "rot_y",
+        "motion_outlier1",
+        "motion_outlier2",
+    ]
diff --git a/halfpipe2bids/utils.py b/halfpipe2bids/utils.py
index 3d9cd79..49dcf8a 100644
--- a/halfpipe2bids/utils.py
+++ b/halfpipe2bids/utils.py
@@ -4,6 +4,7 @@
 import logging
 from nilearn.signal import clean
 from nilearn import plotting
+import re
 
 hp2b_log = logging.getLogger("halfpipe2bids")
 hp2b_url = "https://github.com/pbergeret12/HalfPipe2Bids/"
@@ -45,6 +46,22 @@ def get_strategy_confounds(spec_path):
     return strategy_confounds
 
 
+def regex_to_regressor(regex_confounds, confounds_columns):
+    """
+    Convert the list of regex patterns from HALFpipe to a list of regressors.
+    Args:
+        regex_confounds (list): List of regex patterns.
+        confounds_columns (list): List of column names from confound file.
+    Returns:
+        list: List of confound columns based on fmriprep confound file.
+    """
+    # TODO: To be merged with get_strategy_confounds
+
+    # Compile the regex pattern
+    pattern = re.compile("|".join(regex_confounds))
+    return [col for col in confounds_columns if pattern.fullmatch(col)]
+
+
 def impute_and_clean(df):
     # TODO: documentation and what's the imputation method?
     row_means = df.mean(axis=1, skipna=True)