diff --git a/decoding_tf_analysis.py b/decoding_tf_analysis.py
index 2c53dd3..1b03368 100644
--- a/decoding_tf_analysis.py
+++ b/decoding_tf_analysis.py
@@ -46,6 +46,7 @@ def events_to_labels(evts, events_dict, mask=None):  # TODO Test schreiben
 def permutation_test(baseline, score, n_iter):
     """
     An implementation of a permutation test for classification scores.
+
     :param baseline: The classification scores of the baseline, i.e. selection by chance
     :param score: The classification scores which are tested for significance
     :param n_iter: number of permutations
@@ -120,6 +121,8 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
     # Compute index of time point 0
     index = math.floor((len(metric[0]) / time_scale) * 100)
     baseline = np.array(metric[:index]).flatten()
+
+    # Plot the result
     plt.plot(np.linspace(-200, 1000, 1127), np.mean(metric, axis=0))
     plt.ylabel('Accuracy (%)')
     plt.xlabel('Time (ms)')
@@ -129,11 +132,12 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
     # Compute the permutation tests
     for t in range(len(metric[0][index:])):
         score_t = np.asarray(metric[:, t + index])
-        p = permutation_test(baseline, score_t, 100)
+        p = permutation_test(baseline, score_t, 1000)
         p_values.append(p)
         if t % 50 == 0:
             print(str(t) + " Out of " + str(len(metric[0][index:])))
 
+    # Plot the result
     plt.plot(times[index:], p_values)
     plt.ylabel('P-Value')
     plt.xlabel('Time (ms)')
@@ -143,14 +147,17 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
 
 def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=None, plot=False):
     """
-    Compute the time frequency representation (TFR) of data for a given condition via morlet wavelets
+    Compute the time frequency representation (TFR) of data for a given condition via Morlet wavelets
+
     :param raw: the data
-    :param condition: the condition for which to compute the TFR. Given as a list of tuples of the form (stimulus, condition) # TODO ambiguous use of condition
+    :param condition: the condition for which to compute the TFR. Given as a list of tuples of the form (stimulus, texture)
     :param freqs: the frequencies for which to compute the TFR
-    :param n_cycles: the number of cycles used by the morlet wavelets
-    :param response: type of expected TFR. Can be total, induced or evoked. Default is induced
+    :param n_cycles: the number of cycles used by the Morlet wavelets
+    :param response: type of expected TFR. Can be total, induced or evoked. Default is induced,
+                    the others were not used for the report, only for exploration
     :param baseline: baseline used to correct the power. A tuple of the form (start, end).
-        Default is None and no baseline correction will be applid
+        Default is None and no baseline correction will be applied
+    :param plot: True if results should be plotted, else false.
     :return: The TFR or the given data for a given condition. Has type AverageTFR
     """
     epochs, _ = get_epochs(raw, condition, tmin=-0.2, tmax=1)
@@ -168,6 +175,7 @@ def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=Non
         power_induced = tfr_morlet(epochs.subtract_evoked(), freqs=freqs, n_cycles=n_cycles, return_itc=False, n_jobs=4)
         power = mne.combine_evoked([power_total, power_induced], weights=[1, -1])
     if plot: power.plot(picks='P7')
+    # Apply a baseline correction to the power data
     power.apply_baseline(mode='ratio', baseline=baseline)
     if plot:
         plot_oscillation_bands(power)
@@ -185,13 +193,9 @@ def time_frequency(dataset, filename, compute_tfr=True):
     :param compute_tfr: If True the TFRs will be created, else the TFRs will be loaded from a precomputed file
     """
     # Parameters
-    # Frequency space (from, to, steps) -> Control frequency resolution : Between num=50-80 good for 1-50Hz
     # freqs = np.linspace(0.1, 50, num=50) # Use this for linear space scaling
     freqs = np.logspace(*np.log10([0.1, 50]), num=50)
-    # Number of cycles -> Controls time resolution ? At ~freqs/2 good for high frequency resolution
-    n_cycles = freqs / 2  # 1 for high time resolution & freq smoothing, freqs/2 for high freq resolution & time smooth
-    # Baseline -> Should not go post-stimulus, i.e. > 0 -> Best ist pre-stimulus (e.g. -400 to -200ms)
-    baseline = [-0.5, 0]
+    n_cycles = freqs / 2
     cond1 = []
     cond2 = []
     times = None
@@ -209,6 +213,8 @@ def time_frequency(dataset, filename, compute_tfr=True):
             raw.set_montage('standard_1020', match_case=False)
 
             # Create the two conditions we want to compare
+            # IMPORTANT: If different conditions should be compared you have to change them here, by altering the second
+            # argument passed to create_tfr
             power_cond1 = create_tfr(raw, [('face', 'intact')], freqs, n_cycles, 'induced', (-0.2, 0))
             print('    CONDITION 1 LOADED')
             cond1.append(power_cond1)
@@ -219,17 +225,23 @@ def time_frequency(dataset, filename, compute_tfr=True):
             cond2.append(power_cond2)
             print('    DONE')
 
+        # Save the data so we can access the results more easily
         np.save('cached_data/tf_data/' + filename + '_cond1', cond1)
         np.save('cached_data/tf_data/' + filename + '_cond2', cond2)
     else:
+        # If the data should not be recomputed, load the given filename
         cond1 = np.load('cached_data/tf_data/' + filename + '_cond1.npy', allow_pickle=True).tolist()
         cond2 = np.load('cached_data/tf_data/' + filename + '_cond2.npy', allow_pickle=True).tolist()
     if times is None:
         times = cond1[0].times
+
+    # Some plots
     mne.grand_average(cond1).plot(picks=['P7'], vmin=-3, vmax=3, title='Grand Average P7')
     mne.grand_average(cond2).plot(picks=['P7'], vmin=-3, vmax=3, title='Grand Average P7')
     plot_oscillation_bands(mne.grand_average(cond1))
     plot_oscillation_bands(mne.grand_average(cond2))
+
+    # Compute the cluster permutation
     F, clusters, cluster_p_values, h0 = mne.stats.permutation_cluster_test(
         [mne.grand_average(cond1).data, mne.grand_average(cond2).data], n_jobs=4, verbose='INFO',
         seed=123)
diff --git a/erp_analysis.py b/erp_analysis.py
index a27cea4..3ec224e 100644
--- a/erp_analysis.py
+++ b/erp_analysis.py
@@ -93,16 +93,18 @@ def create_peak_difference_feature(df, max_subj=40):
 
 def analyze_erp(channels, precompute=True):
     """
-    Execute several statistical tests for different hypothesis, to analyze ERPs
+    Execute several statistical tests for different hypothesis, to analyse ERPs
     :param channels: The channels for which the tests are executed
     :param precompute: If true, the peak-difference data will be computed. Else it will be loaded from a precomputed file,
                         if it exists. This should only be set 'False' if the method was already executed once!
     """
     if precompute:
+        # Precompute the erp peaks
         precompute_erp_df('N170')
 
     for c in channels:
         print("CHANNEL: " + c)
+        # Load the erp peak data and create the features for the t-tests
         erp_df = pd.read_csv('cached_data/erp_peaks/erp_peaks_' + c + '.csv', index_col=0)
         feature_df = create_peak_difference_feature(erp_df)
         # 1. H_a : There is a difference between the N170 peak of recognizing faces and cars
diff --git a/preprocessing_and_cleaning.py b/preprocessing_and_cleaning.py
index 39df004..2e91fb7 100644
--- a/preprocessing_and_cleaning.py
+++ b/preprocessing_and_cleaning.py
@@ -52,7 +52,7 @@ def filter_data(raw):
     """
     Filter the data of a single subject with a bandpass filter.
     The lower bound ist 0.5Hz to compensate the slow drifts.
-    The upper bound is 50Hz to compensate the high frequencies, including the power line spike at 60Hz
+    The upper bound is 48Hz to compensate the high frequencies, including the power line spike at 60Hz
     :param raw: The data to be filtered
     :return: The filtered data
     """
diff --git a/utils/file_utils.py b/utils/file_utils.py
index f8b6236..146710f 100644
--- a/utils/file_utils.py
+++ b/utils/file_utils.py
@@ -18,7 +18,7 @@ def load_bad_annotations(filepath, fileending="badSegments.csv"):
 
 def load_preprocessed_data(subject, dataset):
     """
-    Load the raw object as well as the annotations of the preprocessed file
+    Load the raw object of the preprocessed file
     :param subject: The subject, for which we want to load the raw object
     :param dataset: The currently viewed dataset
     :param selected_subjects: The manually preprocessed subjects
diff --git a/utils/plot_utils.py b/utils/plot_utils.py
index 349172e..3503a2c 100644
--- a/utils/plot_utils.py
+++ b/utils/plot_utils.py
@@ -58,7 +58,8 @@ def plot_grand_average(dataset):
 
 def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
     """
-    Plot teh F-Statistic values of permutation clusters with p-values <= 0.05 in color and > 0.05 in grey.
+    Plot the F-Statistic values of permutation clusters with p-values <= 0.05 in color and > 0.05 in grey.
+    Currently only works well for the linear scaling. For the logarithmic scaling a different x-axis has to be chosen
 
     :param F: F-Statistics of the permutation clusters
     :param clusters: all permutation clusters
@@ -81,9 +82,19 @@ def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
 
 
 def plot_oscillation_bands(condition):
+    """
+        Plot the oscillation bands for a given condition in the time from 130ms to 200ms
+        :param condition: the condition to plot the oscillation bands for
+        """
     fig, axis = plt.subplots(1, 5, figsize=(25, 5))
-    condition.plot_topomap(baseline=(-0.2, 0), fmin=0, fmax=4, title='Delta', axes=axis[0], show=False, vmin=0, vmax=1.5, tmin=0, tmax=1)
-    condition.plot_topomap(baseline=(-0.2, 0), fmin=4, fmax=8, title='Theta', axes=axis[1], show=False, vmin=0, vmax=0.7, tmin=0, tmax=1)
-    condition.plot_topomap(baseline=(-0.2, 0), fmin=8, fmax=12, title='Alpha', axes=axis[2], show=False, vmin=-0.15, vmax=0.2, tmin=0, tmax=1)
-    condition.plot_topomap(baseline=(-0.2, 0), fmin=13, fmax=30, title='Beta', axes=axis[3], show=False, vmin=-0.18, vmax=0.2, tmin=0, tmax=1)
-    condition.plot_topomap(baseline=(-0.2, 0), fmin=30, fmax=45, title='Gamma', axes=axis[4], vmin=0, vmax=0.2, tmin=0, tmax=1)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=0, fmax=4, title='Delta', axes=axis[0], show=False, vmin=0,
+                           vmax=1.5, tmin=0.13, tmax=0.2)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=4, fmax=8, title='Theta', axes=axis[1], show=False, vmin=0,
+                           vmax=0.7, tmin=0.13, tmax=0.2)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=8, fmax=12, title='Alpha', axes=axis[2], show=False, vmin=-0.25,
+                           vmax=0.2, tmin=0.13, tmax=0.2)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=13, fmax=30, title='Beta', axes=axis[3], show=False, vmin=-0.21,
+                           vmax=0.2, tmin=0.13, tmax=0.2)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=30, fmax=45, title='Gamma', axes=axis[4], vmin=-0.05, vmax=0.2,
+                           tmin=0.13,
+                           tmax=0.2)