„README.md“ ändern

.gitignore

@@ -0,0 +1,6 @@
+# Ignore the n170 dataset, so that it wont be pushed onto the repo
+/Dataset/n170
+
+# Ignore unnecessary files
+/utils/__pycache__
+.idea/

Dataset/README.md

@@ -0,0 +1,4 @@
+This folder should hold the n170 dataset.
+Unpack the dataset here, so that the file structure: 'Dataset/n170/...' exists.
+
+Bad annotations from the manually preprocessing step are saved in the 'preprocessed' folder.

Dataset/preprocessed/sub-001_task-N170_badannotations.csv

@@ -0,0 +1,24 @@
+onset,duration,description
+1970-01-01 00:00:0.0,0,
+1970-01-01 00:00:03.792590,4.272874611801244,BAD_
+1970-01-01 00:01:16.632102,4.703474378881992,BAD_
+1970-01-01 00:01:25.812306,2.674687014751555,BAD_
+1970-01-01 00:01:30.096020,3.3330078125,BAD_
+1970-01-01 00:01:39.164434,2.2565083947981464,BAD_
+1970-01-01 00:01:42.936322,3.154971370341613,BAD_
+1970-01-01 00:02:53.246060,5.229302940605606,BAD_
+1970-01-01 00:03:06.573345,2.070191187888213,BAD_
+1970-01-01 00:03:13.224567,8.847997137034156,BAD_
+1970-01-01 00:03:24.367312,6.69499830163042,BAD_
+1970-01-01 00:03:50.203670,2.9893560753105817,BAD_
+1970-01-01 00:04:51.697013,9.613967876552806,BAD_
+1970-01-01 00:05:01.312749,2.7326523680124524,BAD_
+1970-01-01 00:06:17.397529,5.784114178959612,BAD_
+1970-01-01 00:06:25.472251,3.2791828416148974,BAD_
+1970-01-01 00:06:31.833646,9.137823903338472,BAD_
+1970-01-01 00:06:40.977378,2.455246748835407,BAD_
+1970-01-01 00:07:06.623277,2.152998835403764,BAD_
+1970-01-01 00:07:26.697804,1.9542604813664184,BAD_
+1970-01-01 00:09:02.732109,4.980879998058981,BAD_
+1970-01-01 00:09:15.452340,1.8755932162266618,BAD_
+1970-01-01 00:11:20.352343,2.4966505725932393,BAD_

Dataset/preprocessed/sub-003_task-N170_badannotations.csv

@@ -0,0 +1,10 @@
+onset,duration,description
+1970-01-01 00:00:0.0,0,
+1970-01-01 00:02:17.128157,2.947952251552806,BAD_
+1970-01-01 00:02:20.960569,7.62244395380435,BAD_
+1970-01-01 00:03:30.145704,5.332812500000017,BAD_
+1970-01-01 00:04:40.074527,5.490147030279502,BAD_
+1970-01-01 00:05:49.665420,3.3247270477484676,BAD_
+1970-01-01 00:06:59.603314,11.209782608695662,BAD_
+1970-01-01 00:08:12.657335,8.032341809006198,BAD_
+1970-01-01 00:09:22.031561,2.3020526009315745,BAD_

Dataset/preprocessed/sub-014_task-N170_badannotations.csv

@@ -0,0 +1,9 @@
+onset,duration,description
+1970-01-01 00:00:0.0,0,
+1970-01-01 00:00:00.886042,2.5421947787267083,BAD_
+1970-01-01 00:01:18.262621,2.268929541925459,BAD_
+1970-01-01 00:03:27.443617,0.9647090935558822,BAD_
+1970-01-01 00:04:46.825909,3.2253578707297947,BAD_
+1970-01-01 00:05:52.347597,2.637423573369574,BAD_
+1970-01-01 00:06:59.515191,2.4345448369564906,BAD_
+1970-01-01 00:08:07.230491,1.287658918866498,BAD_

README.md

@@ -1,16 +1,55 @@
-## Semesterproject of the lecture "Semesterproject Signal processing and Analysis
+## Semesterproject of the lecture "Semesterproject Signal processing and Analysis of human brain potentials (eeg)" WS 2020/21
-of human brain potentials (eeg) WS 2020/21
 
-This repository holds the code of the semesterproject as well as the report.
+This repository holds the code of the semesterproject as well as the report, created by Julius Voggesberger.
-The main files are 'preprocessing_and_cleaning.py', 'erp_analysis.py' and 'decoding_tf_analyis.py'.
+As the dataset for the project, the N170-dataset was chosen.
-The files hold:
+As the three subjects, to be manually pre-processed, the subjects 001, 003 and 014 were chosen.
-- preprocessing_and_cleaning.py : Holds the pre-processing pipeline of the project. By executing the file all subjects are pre-processed. Subjects 001, 003, 014 are pre-processd with manually selected pre-processing information, all other subjects are pre-processed with the given pre-processing information. Details can be found in the comments of the code.
+The rest of the subjects were pre-processed with provided pre-processing information.
-- erp_analysis.py : Hold the code for the erp-analysis. Computes the peak-differences and t-tests for several experimental contrasts. Details can be found in the comments of the code.
-- decoding_tf_analysis.py : Holds the code for the decoding and time-frequency analysis. Details can be found in the comments of the code.
 
-The folder 'utils' holds helper functions for some plots needed for the analysis and to load data, generate strings etc. and holds the code given in the lecture.
+### Structure
-The folder 'test' holds mostly unittests that test helper functions and one function which visually checks if N170 peaks are extracted correctly.
+```
+├── Dataset: The dataset of the project as well as the manually selected bad segments are stored here. 
+|   ├── n170: Store the dataset here. 
+|   └── preprocessed: Bad segments are stored here. 
+├── cached_data: Data that is generated in the analysis part is stored here. 
+|   ├── decoding_data: Results of the classifiers. 
+|   ├── erp_peaks: ERP peaks needed for the ERP analysis. 
+|   └── tf_data: Time-frequency data needed for the tf-analysis. 
+├── test: Contains unittests and one visual check. 
+├── utils: Contains helper methods 
+|   ├── ccs_eeg_semesterproject: Methods given in the lecture. 
+|   ├── ccs_eeg_utils_reduced: Method for reading in BIDS provided in the lecture. 
+|   ├── file_utils.py: Methods for reading in files and getting epochs. 
+|   └── plot_utils.py: Methods for manually created plots. 
+├── preprocessing_and_cleaning.py: The preprocessing pipeline. 
+├── erp_analysis.py: The ERP-Analysis and computation of ERP peaks.
+├── decoding_tf_analysis.py: Decoding and time-frequency analysis.
+└── semesterproject_report_voggesberger: The report of the project.
+```
 
-For the code to work properly, the N170 dataset needs to be provided.
+### Running the project
-When first running the analysis, it may take a while. After running it one time the data is cached, so that it can be reused if the analysis should be executed again. Be careful though, as a parameter has to be explicitly set in the code, so that the already computed data is used. This parameter is a boolean given to each analysis function which caches data.
+To run the project python 3.7 is required and anaconda recommended.\
+To ensure reproducability, randomstates were used for methods which are non-deterministic.
+The randomstates used are either '123' or '1234'.\
+The following libraries are needed:
+- Matplotlib 3.3.3
+- MNE 0.22.0
+- MNE-Bids 0.6
+- Numpy 1.19.4
+- Scikit-Learn 0.23.2
+- Pandas 1.2.0
+- Scipy 1.5.4
 
+For the code to work, the N170 dataset needs to be provided and put into the folder 'Dataset/n170/', so that the file structure 'Dataset/n170/sub-001', etc. exists.
+The pre-processed raw objects are saved in their respective subject folder, in 'Dataset/n170/'.
+When first running the analysis, it may take a while. 
+After running it one time the data is cached, so that it can be reused if the analysis should be executed again at a later time.
+For the cached data to be used, a boolean parameter has to be set in the respective analysis method.
+
+It may be necessary to set the parent directory 'semesterproject_lecture_eeg' as 'Sources Root' for the project, if pycharm is used as an IDE.
+
+### Parameters
+Parameters have to be changed manually in the code, if different settings want to be tried.
+
+### Visualisation
+The visualisation methods that were used to generate the visualisations in the report, are contained in the code, if they were created manually.
+If a visualisation method from mne was used to create the visualisation, it may exist in the code or not.

cached_data/README.md

@@ -0,0 +1 @@
+This folder holds cached data.

cached_data/decoding_data/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

cached_data/erp_peaks/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

cached_data/tf_data/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

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
@@ -111,6 +112,7 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
             if times is None:
                 times = epochs.times
         np.save('cached_data/decoding_data/' + filename, metric)
+        metric = np.asarray(metric)
     else:
         # Dummy time which is created according to epoch.times
         times = np.linspace(-0.09960938, 1, 1127)
@@ -119,6 +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)')
@@ -133,6 +137,7 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
         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)')
@@ -140,16 +145,19 @@ def decoding(dataset, filename, compute_metric=True, mask=None):
     plt.show()
 
 
-def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=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 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 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)
@@ -166,14 +174,16 @@ def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=Non
         power_total = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, return_itc=False, n_jobs=4)
         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])
-    # power.plot(picks='P7')
+    if plot: power.plot(picks='P7')
+    # Apply a baseline correction to the power data
     power.apply_baseline(mode='ratio', baseline=baseline)
-    # plot_oscillation_bands(power)
+    if plot:
-    # power.plot(picks='P7')
+        plot_oscillation_bands(power)
+        power.plot(picks='P7')
     return power
 
 
-def time_frequency(dataset, filename, compute_tfr=True):
+def time_frequency(dataset, filename, scaling='lin', compute_tfr=True):
     """
     Runs time frequency analysis
 
@@ -181,15 +191,14 @@ 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
-    # Frequency space (from, to, steps) -> Control frequency resolution : Between num=50-80 good for 1-50Hz
+    if scaling == 'lin':
-    # freqs = np.linspace(0.1, 50, num=50)  #
+        freqs = np.linspace(0.1, 50, num=50)  # Use this for linear space scaling
-    freqs = np.logspace(*np.log10([0.1, 50]), num=50)
+    else:
-    # Number of cycles -> Controls time resolution ? At ~freqs/2 good for high frequency resolution
+        freqs = np.logspace(*np.log10([0.1, 50]), num=50)
-    n_cycles = freqs / 2  # 1 for high time resolution & freq smoothing, freqs/2 for high freq resolution & time smooth
+    n_cycles = freqs / 2
-    # Baseline -> Should not go post-stimulus, i.e. > 0 -> Best ist pre-stimulus (e.g. -400 to -200ms)
-    baseline = [-0.5, 0]
     cond1 = []
     cond2 = []
     times = None
@@ -207,6 +216,8 @@ 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)
@@ -217,26 +228,32 @@ 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
-    # 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')
+    # 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)
-    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_100iters', False)
+    decoding(ds, 'faces_vs_cars', True)
-    # time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', False)
+    time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', 'log', True)
-    time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', False)
+

erp_analysis.py

@@ -91,13 +91,20 @@ def create_peak_difference_feature(df, max_subj=40):
     return peak_diff_df
 
 
-def analyze_erp(channels):
+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
@@ -130,5 +137,4 @@ def analyze_erp(channels):
 
 if __name__ == '__main__':
     mne.set_log_level(verbose=VERBOSE_LEVEL)
-    # precompute_erp_df('N170')
+    analyze_erp(['P7', 'PO7', 'P8', 'PO8'], True)
-    analyze_erp(['P7', 'PO7', 'P8', 'PO8'])

plotting.py

@@ -1,115 +0,0 @@
-import mne
-import matplotlib.pyplot as plt
-
-from mne.preprocessing import create_eog_epochs
-from mne_bids import BIDSPath, read_raw_bids
-from utils.ccs_eeg_utils import read_annotations_core
-from utils.file_utils import get_epochs
-
-
-def load_unprocessed_subject(subject, dataset):
-    """
-    Load the eeg data of a subject
-    :param subject: The subject of which the data will be loaded
-    :param dataset: The dataset which will be loaded
-    :return: the subject data
-    """
-    bids_path = BIDSPath(subject=subject, task=dataset, session=dataset, datatype='eeg', suffix='eeg',
-                         root='Dataset\\' + dataset)
-    raw = read_raw_bids(bids_path)
-    # Add annotations
-    read_annotations_core(bids_path, raw)
-    return raw
-
-
-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
-    :param raw: The data to be filtered
-    :return: The filtered data
-    """
-    raw.filter(0.5, 48, fir_design='firwin')
-    return raw
-
-
-def plot_filter_data():
-    ds = 'N170'
-    for subj in ['014']:
-        data = load_unprocessed_subject(subj, ds)
-        data.load_data()
-        # data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
-        fig = mne.viz.plot_raw_psd(data, fmax=80, average=True, show=False)
-        fig.savefig("plots/frequency_nonfiltered_subj_" + subj)
-        filter_data(data)
-        fig = mne.viz.plot_raw_psd(data, fmax=80, average=True, show=False)
-        fig.savefig("plots/frequency_filtered_subj_" + subj + "_48Hz.png")
-        # data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
-
-
-def plot_filter_data_epoched(subj):
-    ds = 'N170'
-    data = load_unprocessed_subject(subj, ds)
-    data.load_data()
-    filter_data(data)
-    data.annotations.delete(list(range(0, len(data.annotations))))
-    data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
-    get_epochs(data)[0].average().plot()
-
-
-def plot_cleaning():
-    ds = 'N170'
-    for subj in ['014']:
-        data = load_unprocessed_subject(subj, ds)
-        data.load_data()
-        filter_data(data)
-        folder = "Dataset\\" + ds + "\\sub-" + subj + "\\ses-" + ds + "\\eeg\\"
-        filepath = folder + "sub-" + subj + "_task-" + ds
-        print(filepath)
-        ann = mne.read_annotations(filepath + "_" + "badannotations.csv")
-        data.annotations.append(ann.onset, ann.duration, ann.description)
-        data.plot(n_channels=len(data.ch_names), block=True, scalings=40e-6)
-
-
-def plot_ica():
-    ds = 'N170'
-    for subj in ['014']:
-        data = load_unprocessed_subject(subj, ds)
-        folder = "Dataset\\" + ds + "\\sub-" + subj + "\\ses-" + ds + "\\eeg\\"
-        filepath = folder + "sub-" + subj + "_task-" + ds
-        ann = mne.read_annotations(filepath + "_" + "badannotations.csv")
-        data.annotations.append(ann.onset, ann.duration, ann.description)
-        data.load_data()
-        data.set_channel_types({'HEOG_left': 'eog', 'HEOG_right': 'eog', 'VEOG_lower': 'eog'})
-        data.set_montage('standard_1020', match_case=False)
-        ica_raw = data.copy()
-        ica_raw.filter(l_freq=1, h_freq=None)
-
-        # Then run ICA
-        ica = mne.preprocessing.ICA(method="fastica",
-                                    random_state=123)  # Use a random state for reproducable results #TODO Old Random state 123 or new one?
-        ica.fit(ica_raw, verbose=True)
-        ica_raw.load_data()
-        # ica.plot_components(inst=ica_raw, ch_type='eeg', contours=0, topomap_args={'extrapolate': 'head'},
-        #                    psd_args={'fmin': 0, 'fmax': 80})
-        ica.plot_sources(ica_raw)
-        ica.plot_properties(inst=ica_raw, dB=False, topomap_args={'extrapolate': 'head', 'contours': 0},
-                            psd_args={'fmin': 3, 'fmax': 50}, picks=['eeg'])
-
-
-def plot_joint_eog_plots():
-    ds = 'N170'
-    for subj in ['014']:
-        data = load_unprocessed_subject(subj, ds)
-        data.load_data()
-        data.set_channel_types({'HEOG_left': 'eog', 'HEOG_right': 'eog', 'VEOG_lower': 'eog'})
-        data.set_montage('standard_1020', match_case=False)
-        eog_evoked = create_eog_epochs(data).average()
-        eog_evoked.apply_baseline(baseline=(None, -0.2))
-        eog_evoked.plot_joint()
-
-
-# plot_ica()
-# plot_joint_eog_plots()
-plot_filter_data_epoched('003')

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
     """
@@ -70,8 +70,7 @@ def clean_data(raw, subject, dataset, cleaned=False):
     :return: the bad channels
     """
     channels = None
-    folder = "Dataset\\" + dataset + "\\sub-" + subject + "\\ses-" + dataset + "\\eeg\\"
+    filepath = "Dataset/preprocessed/sub-" + subject + "_task-" + dataset + "_badannotations.csv"
-    filepath = folder + "sub-" + subject + "_task-" + dataset
 
     # If nothing was marked yet, plot the data to mark bad segments
     if not cleaned:
@@ -80,15 +79,15 @@ def clean_data(raw, subject, dataset, cleaned=False):
         bad_idx = [idx for (idx, annot) in enumerate(raw.annotations) if annot['description'] == "BAD_"]
         # If bad intervals were found save
         if bad_idx:
-            raw.annotations[bad_idx].save(filepath + "_badannotations.csv")
+            raw.annotations[bad_idx].save(filepath)
 
-    if os.path.isfile(filepath + "_badannotations.csv"):
+    if os.path.isfile(filepath):
-        annotations = mne.read_annotations(filepath + "_badannotations.csv")
+        annotations = mne.read_annotations(filepath)
         raw.annotations.append(annotations.onset, annotations.duration, annotations.description)
 
     # Set the bad channels for each subject
     if subject == '001':
-        channels = ['F8']  # Maybe also FP2?
+        channels = ['F8']
     elif subject == '003':
         channels = []
     elif subject == '014':
@@ -119,8 +118,6 @@ def run_ica(raw, dataset, subject, search='manual'):
 
     if search == 'manual':
         ica_raw.load_data()
-        # ica.plot_components(inst=ica_raw, ch_type='eeg', contours=0, topomap_args={'extrapolate': 'head'},
-        #                    psd_args={'fmin': 0, 'fmax': 80})
         ica.plot_properties(inst=ica_raw, dB=False, topomap_args={'extrapolate': 'head', 'contours': 0},
                             psd_args={'fmin': 0, 'fmax': 50}, picks=['eeg'])
         ica.plot_sources(ica_raw)

test/visual_sanity_checks.py

@@ -1,4 +1,6 @@
-from utils.file_utils import load_preprocessed_data, get_epochs
+import mne
+
+from utils.file_utils import get_epochs
 
 
 def check_peaks():
@@ -6,13 +8,16 @@ def check_peaks():
     Sanity check for the "get_peaks" method
     """
     import matplotlib.pyplot as plt
-    raw = load_preprocessed_data('002', 'N170')
+    subject = '001'
+    folder = "../Dataset/n170/sub-" + subject + "/ses-n170/eeg/"
+    filepath = folder + "sub-" + subject + "_task-n170"
+    raw = mne.io.read_raw_fif(filepath + "_cleaned.fif")
     epochs, _ = get_epochs(raw, [('face', 'intact')], picks='P7')
     ch, latency, peak = epochs.average().get_peak(tmin=0.13, tmax=0.2, mode='neg', return_amplitude=True)
     import numpy as np
     plt.plot(epochs.times, np.squeeze(epochs.average().data.T))
-    plt.vlines([0.13, 0.2], -0.00001, 0.00001, colors='r', linestyles='dotted')
+    plt.vlines([0.13, 0.2], -0.00001, 0.00001, colors='gray', linestyles='dotted')
-    plt.vlines(latency, -0.00001, 0.00001, colors='gray', linestyles='dotted')
+    plt.vlines(latency, -0.00001, 0.00001, colors='r', linestyles='dotted')
     plt.show()
 
 

utils/file_utils.py

@@ -18,13 +18,13 @@ 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
     :return: The raw object
     """
-    folder = "Dataset\\" + dataset + "\\sub-" + subject + "\\ses-" + dataset + "\\eeg\\"
+    folder = "Dataset/" + dataset + "/sub-" + subject + "/ses-" + dataset + "/eeg/"
     filepath = folder + "sub-" + subject + "_task-" + dataset
     raw = mne.io.read_raw_fif(filepath + "_cleaned.fif")
     return raw

utils/plot_utils.py

@@ -56,15 +56,17 @@ 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 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
     :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):
@@ -74,16 +76,33 @@ 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
+        :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=0, fmax=4, title='Delta', axes=axis[0], show=False, vmin=0,
-    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)
+                           vmax=1.5, 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=-0.15, vmax=0.2, tmin=0, tmax=1)
+    condition.plot_topomap(baseline=(-0.2, 0), fmin=4, fmax=8, title='Theta', axes=axis[1], show=False, vmin=0,
-    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)
+                           vmax=0.7, tmin=0.13, tmax=0.2)
-    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=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)