diff --git a/cognitive_load _analysis/adicht_utils.py b/cognitive_load _analysis/adicht_utils.py
index 26f6350632708570cfa556dce803a354a9ca0076..2a7275a19694dd9ba195193c18240769afce1099 100644
--- a/cognitive_load _analysis/adicht_utils.py	
+++ b/cognitive_load _analysis/adicht_utils.py	
@@ -169,7 +169,7 @@ def atmung_process(atmung_signal_:np.ndarray, sampling_rate_=1000):
     #peak_signals, info = nk.rsp_peaks(rsp_cleaned)
     #amplitude = nk.rsp_amplitude(rsp_cleaned, peak_signals)
     # TODO perform process and select breath rate or RVT
-    rsp_rate = nk.rsp_rate(rsp_cleaned, sampling_rate=sampling_rate_)
+    rsp_rate =nk.rsp_rate(rsp_cleaned, sampling_rate=sampling_rate_)
     return rsp_rate
 
 
@@ -339,7 +339,7 @@ def slice_channels(channels: list, indx_start: np.ndarray, indx_end: np.ndarray,
                     post_process = 2 * post_process
                     data = slice_signals(channel, indx_start, indx_end, take_mean, post_process)
                 if channel.name == "Atmung":
-                    post_process = 3 * post_process
+                    post_process = 3 * post_processt
                     data = slice_signals(channel, indx_start, indx_end, take_mean, post_process)
                 else:
                     data = slice_signals(channel, indx_start, indx_end)
@@ -349,7 +349,8 @@ def slice_channels(channels: list, indx_start: np.ndarray, indx_end: np.ndarray,
                 if mean == False:
                     normalized_data = np.subtract(data,data[0]).astype(np.float32)
                 else:
-                    normalized_data = data - data[0]
+                    #normalized_data = data - data[0] # uncomment for baseline subraction
+                    normalized_data = data
                 channels_df[channel.name] = np.delete(normalized_data, 0)
 
     return channels_df, Error
@@ -406,10 +407,10 @@ def slice_channels_for_analysis(channels: list, indx_start: np.ndarray, indx_end
             else:
                 data = slice_signals(channel, indx_start, indx_end, take_mean, post_process)
 
-            #  baseline subtraction
+            #  uncomment for baseline subtraction
             if base_normalization:
                 baseline = np.mean(data[0])
-                data = data - baseline
+                #data = data - baseline
                 #data = np.subtract(data, baseline).astype(np.float16)
                 #data = signal.detrend(data, axis=0, type="constant", bp= baseline,overwrite_data=False)
 
diff --git a/cognitive_load _analysis/smell_load_analysis.py b/cognitive_load _analysis/smell_load_analysis.py
index 4080a685a0971795e59ad248b6a11707e138ea64..1fc49323293aad6c84b22fb78929ff8a569798ad 100644
--- a/cognitive_load _analysis/smell_load_analysis.py	
+++ b/cognitive_load _analysis/smell_load_analysis.py	
@@ -55,7 +55,7 @@ def read_adicht_file(file: str, front_offset: int = 24e3, back_offset: int = 24e
     slice_end = np.delete(slice_end, baseline_ids[0, 1])
 
     # replacing baseline tick positions
-    slice_start.put(baseline_ids[0, 0], 0)
+    slice_start.put(baseline_ids[0, 0], 1)
     slice_end.put(0, baseline_tickpositions[0, 1])
 
     # Removing baseline_end tick positions
@@ -88,7 +88,7 @@ def get_data_for_analysis(directory: str = "smell_adicht_files/",
                           channels: list = ['Atmung', 'Hautleitfähigkeit', 'HR'],
                           start_block: list = ['baseline_start'],
                           end_block: list = ['baseline_end'], offeset_sec: int = 24,
-                          return_signals:bool=True) \
+                          return_signals:bool=False) \
         -> [np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Main function that collects data from all subjects