diff --git a/decoding_tf_analysis.py b/decoding_tf_analysis.py
index 1b03368..ca75f86 100644
--- a/decoding_tf_analysis.py
+++ b/decoding_tf_analysis.py
@@ -183,7 +183,7 @@ def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=Non
     return power
 
 
-def time_frequency(dataset, filename, compute_tfr=True):
+def time_frequency(dataset, filename, scaling='lin', compute_tfr=True):
     """
     Runs time frequency analysis
 
@@ -191,10 +191,13 @@ def time_frequency(dataset, filename, compute_tfr=True):
     :param filename: Filename of either the file from which the TFRs will be loaded
         or to which they will be saved
     :param compute_tfr: If True the TFRs will be created, else the TFRs will be loaded from a precomputed file
+    :param scaling: default 'lin' for linear scaling, else can be 'log' for logarithmic scaling
     """
     # Parameters
-    # freqs = np.linspace(0.1, 50, num=50) # Use this for linear space scaling
-    freqs = np.logspace(*np.log10([0.1, 50]), num=50)
+    if scaling == 'lin':
+        freqs = np.linspace(0.1, 50, num=50)  # Use this for linear space scaling
+    else:
+        freqs = np.logspace(*np.log10([0.1, 50]), num=50)
     n_cycles = freqs / 2
     cond1 = []
     cond2 = []
@@ -245,11 +248,11 @@ def time_frequency(dataset, filename, compute_tfr=True):
     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)
-    plot_tf_cluster(F, clusters, cluster_p_values, freqs, times)
+    plot_tf_cluster(F, clusters, cluster_p_values, freqs, times, scaling)
 
 
 if __name__ == '__main__':
     mne.set_log_level(verbose=VERBOSE_LEVEL)
     ds = 'N170'
     decoding(ds, 'faces_vs_cars', True)
-    time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', True)
+    time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', 'log', False)
diff --git a/utils/plot_utils.py b/utils/plot_utils.py
index 3503a2c..92a9723 100644
--- a/utils/plot_utils.py
+++ b/utils/plot_utils.py
@@ -56,7 +56,7 @@ def plot_grand_average(dataset):
                                      linestyles=['solid', 'solid', 'dotted', 'dotted'])
 
 
-def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
+def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times, scaling='lin'):
     """
     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
@@ -66,6 +66,7 @@ def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
     :param cluster_p_values: p-values of the clusters
     :param freqs: frequency domain
     :param times: time domain
+    :param scaling: default 'lin' for linear scaling, else can be 'log' for logarithmic scaling
     """
     good_c = np.nan * np.ones_like(F)
     for clu, p_val in zip(clusters, cluster_p_values):
@@ -75,12 +76,19 @@ def plot_tf_cluster(F, clusters, cluster_p_values, freqs, times):
     bbox = [times[0], times[-1], freqs[0], freqs[-1]]
     plt.imshow(F, aspect='auto', origin='lower', cmap=cm.gray, extent=bbox, interpolation='None')
     a = plt.imshow(good_c, cmap=cm.RdBu_r, aspect='auto', origin='lower', extent=bbox, interpolation='None')
+
+    if scaling == 'log':
+        ticks = [1, 4, 8, 12, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50]
+        labels = [round(freqs[i], 2) for i in range(len(freqs)) if i + 1 in ticks]
+        plt.yticks(ticks, labels)
+
     plt.colorbar(a)
     plt.xlabel('Time (s)')
     plt.ylabel('Frequency (Hz)')
     plt.show()
 
 
+
 def plot_oscillation_bands(condition):
     """
         Plot the oscillation bands for a given condition in the time from 130ms to 200ms