Added some files, updated some files

decoding_tf_analysis.py

@@ -128,7 +128,7 @@ 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:])))
@@ -184,7 +184,8 @@ def time_frequency(dataset, filename, compute_tfr=True):
     """
     # Parameters
     # Frequency space (from, to, steps) -> Control frequency resolution : Between num=50-80 good for 1-50Hz
-    freqs = np.logspace(*np.log10([0.5, 50]), num=50)  #
+    # freqs = np.linspace(0.1, 50, num=50)  #
+    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)
@@ -223,7 +224,8 @@ def time_frequency(dataset, filename, compute_tfr=True):
         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(cond2).plot(picks=['P7'], vmin=-3, vmax=3, title='Grand Average P7')
+    # 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))
     F, clusters, cluster_p_values, h0 = mne.stats.permutation_cluster_test(
@@ -236,4 +238,5 @@ if __name__ == '__main__':
     mne.set_log_level(verbose=VERBOSE_LEVEL)
     ds = 'N170'
     # decoding(ds, 'faces_vs_cars_100iters', False)
+    # time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', False)
     time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', False)

plotting.py

@@ -1,6 +1,7 @@
 import mne
-from mne.preprocessing import create_eog_epochs
+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
@@ -39,6 +40,8 @@ def plot_filter_data():
         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")
@@ -50,6 +53,8 @@ def plot_filter_data_epoched(subj):
     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()
 
 

preprocessing_and_cleaning.py

@@ -3,7 +3,7 @@ import mne
 
 from mne_bids import (BIDSPath, read_raw_bids)
 from utils.ccs_eeg_semesterproject import load_precomputed_badData, load_precomputed_ica
-from utils.ccs_eeg_utils import read_annotations_core
+from utils.ccs_eeg_utils_reduced import read_annotations_core
 
 
 def load_subject(subject, dataset):
@@ -137,11 +137,11 @@ def run_ica(raw, dataset, subject, search='manual'):
             exclude = [0, 2]  # Through eog: 0, 2
         elif subj == '014':
             exclude = [0, 1, 9]  # Through eog: 0,1
-        # ica.plot_overlay(ica_raw, exclude=exclude)  # Plot differences through exclude
-        # ica.exclude = exclude
         # Apply ica to the raw object
         raw.load_data()
-        # ica.plot_overlay(ica_raw, exclude=exclude)
+        # ica.plot_overlay(ica_raw,
+        #                 title="Signals before (red) applying ICA and after (black) applying ICA on subject 003",
+        #                 exclude=exclude)
         raw = ica.apply(raw, exclude=exclude)
         # Lastly save the ica to a file
         folder = "Dataset\\" + dataset + "\\sub-" + subject + "\\ses-" + dataset + "\\eeg\\"

utils/ccs_eeg_utils.py

@@ -1,275 +0,0 @@
-from osfclient import cli
-import os
-from mne_bids.read import _from_tsv, _drop
-from mne_bids import (BIDSPath, read_raw_bids)
-import mne
-import numpy as np
-
-import scipy.ndimage
-import scipy.signal
-from numpy import sin as sin
-
-
-def read_annotations_core(bids_path, raw):
-    tsv = os.path.join(bids_path.directory, bids_path.update(suffix="events", extension=".tsv").basename)
-    _handle_events_reading_core(tsv, raw)
-
-
-def _handle_events_reading_core(events_fname, raw):
-    """Read associated events.tsv and populate raw.
-    Handle onset, duration, and description of each event.
-    """
-    events_dict = _from_tsv(events_fname)
-
-    if ('value' in events_dict) and ('trial_type' in events_dict):
-        events_dict = _drop(events_dict, 'n/a', 'trial_type')
-        events_dict = _drop(events_dict, 'n/a', 'value')
-
-        descriptions = np.asarray([a + ':' + b for a, b in zip(events_dict["trial_type"], events_dict["value"])],
-                                  dtype=str)
-
-        # Get the descriptions of the events
-    elif 'trial_type' in events_dict:
-
-        # Drop events unrelated to a trial type
-        events_dict = _drop(events_dict, 'n/a', 'trial_type')
-        descriptions = np.asarray(events_dict['trial_type'], dtype=str)
-
-    # If we don't have a proper description of the events, perhaps we have
-    # at least an event value?
-    elif 'value' in events_dict:
-        # Drop events unrelated to value
-        events_dict = _drop(events_dict, 'n/a', 'value')
-        descriptions = np.asarray(events_dict['value'], dtype=str)
-    # Worst case, we go with 'n/a' for all events
-    else:
-        descriptions = 'n/a'
-    # Deal with "n/a" strings before converting to float
-    ons = [np.nan if on == 'n/a' else on for on in events_dict['onset']]
-    dus = [0 if du == 'n/a' else du for du in events_dict['duration']]
-    onsets = np.asarray(ons, dtype=float)
-    durations = np.asarray(dus, dtype=float)
-    # Keep only events where onset is known
-    good_events_idx = ~np.isnan(onsets)
-    onsets = onsets[good_events_idx]
-    durations = durations[good_events_idx]
-    descriptions = descriptions[good_events_idx]
-    del good_events_idx
-    # Add Events to raw as annotations
-    annot_from_events = mne.Annotations(onset=onsets,
-                                        duration=durations,
-                                        description=descriptions,
-                                        orig_time=None)
-    raw.set_annotations(annot_from_events)
-    return raw
-
-
-# taken from the osfclient tutorial https://github.com/ubcbraincircuits/osfclienttutorial
-class args:
-    def __init__(self, project, username=None, update=True, force=False, destination=None, source=None, recursive=False,
-                 target=None, output=None, remote=None, local=None):
-        self.project = project
-        self.username = username
-        self.update = update  # applies to upload, clone, and fetch
-        self.force = force  # applies to fetch and upload
-        # upload arguments:
-        self.destination = destination
-        self.source = source
-        self.recursive = recursive
-        # remove argument:
-        self.target = target
-        # clone argument:
-        self.output = output
-        # fetch arguments:
-        self.remote = remote
-        self.local = local
-
-
-def download_erpcore(task="MMN", subject=1, localpath="local/bids/"):
-    project = "9f5w7"  # after recent change they put everything as "sessions" in one big BIDS file
-
-    arguments = args(project)  # project ID
-    for extension in ["channels.tsv", "events.tsv", "eeg.fdt", "eeg.json", "eeg.set"]:
-        targetpath = '/sub-{:03d}/ses-{}/eeg/sub-{:03d}_ses-{}_task-{}_{}'.format(subject, task, subject, task, task,
-                                                                                  extension)
-        print("Downloading {}".format(targetpath))
-        arguments.remote = "\\ERP_CORE_BIDS_Raw_Files/" + targetpath
-        arguments.local = localpath + targetpath
-        cli.fetch(arguments)
-
-
-def simulate_ICA(dims=4):
-    A = [[-0.3, 0.2], [.2, 0.1]]
-    sample_rate = 100.0
-    nsamples = 1000
-    t = np.arange(nsamples) / sample_rate
-
-    s = []
-
-    # boxcars
-    s.append(np.mod(np.array(range(0, nsamples)), 250) > 125)
-    # a triangle staircase + trend
-    s.append((np.mod(np.array(range(0, nsamples)), 100) + np.array(range(0, nsamples)) * 0.05) / 100)
-    if dims == 4:
-        A = np.array([[.7, 0.3, 0.2, -0.5], [0.2, -0.5, -0.2, 0.3], [-.3, 0.1, 0, 0.2], [-0.5, -0.3, -0.2, 0.8]])
-
-        # some sinosoids
-        s.append(np.cos(2 * np.pi * 0.5 * t) + 0.2 * np.sin(2 * np.pi * 2.5 * t + 0.1) + 0.2 * np.sin(
-            2 * np.pi * 15.3 * t) + 0.1 * np.sin(2 * np.pi * 16.7 * t + 0.1) + 0.1 * np.sin(2 * np.pi * 23.45 * t + .8))
-        # uniform noise
-        s.append(0.2 * np.random.rand(nsamples))
-    x = np.matmul(A, np.array(s))
-    return x
-
-
-def spline_matrix(x, knots):
-    # bah, spline-matrices are a pain to implement. 
-    # But package "patsy" with function "bs" crashed my notebook...
-    # Anyway, knots define where the spline should be anchored. The default should work
-    # X defines where the spline set should be evaluated.
-    # e.g. call using: spline_matrix(np.linspace(0,0.95,num=100))
-    import scipy.interpolate as si
-
-    x_tup = si.splrep(knots, knots, k=3)
-    nknots = len(x_tup[0])
-    x_i = np.empty((len(x), nknots - 4))
-    for i in range(nknots - 4):
-        vec = np.zeros(nknots)
-        vec[i] = 1.0
-        x_list = list(x_tup)
-        x_list[1] = vec.tolist()
-        x_i[:, i] = si.splev(x, x_list)
-    return x_i
-
-
-def simulate_TF(signal=1, noise=True):
-    # signal can be 1 (image), 2(chirp) or 3 (steps)
-    import imageio
-
-    if signal == 2:
-        im = imageio.imread('ex9_tf.png')
-
-        im = im[0:60, :, 3] - im[0:60, :, 1]
-        # im = scipy.ndimage.zoom(im,[1,1])
-        im = np.flip(im, axis=0)
-
-        # plt.imshow(im,origin='lower')
-
-        # sig = (scipy.fft.irfft(im.T,axis=1))
-
-        nov = 10;
-        im.shape[0] * 0.5
-        nperseg = 50;
-        im.shape[0] - 1
-        t, sig = scipy.signal.istft(im, fs=500, noverlap=nov, nperseg=nperseg)
-        sig = sig / 300  # normalize
-    elif signal == 3:
-        sig = scipy.signal.chirp(t=np.arange(0, 10, 1 / 500), f0=1, f1=100, t1=2, method='linear', phi=90)
-    elif signal == 1:
-
-        x = np.arange(0, 2, 1 / 500)
-        sig_steps = np.concatenate([1.0 * sin(2 * np.pi * x * 50), 1.2 * sin(2 * np.pi * x * 55 + np.pi / 2),
-                                    0.8 * sin(2 * np.pi * x * 125 + np.pi),
-                                    1.0 * sin(2 * np.pi * x * 120 + 3 * np.pi / 2)])
-
-        sig = sig_steps
-    if noise:
-        sig = sig + 0.1 * np.std(sig) * np.random.randn(sig.shape[0])
-
-    return sig
-
-
-def get_TF_dataset(subject_id='002', bids_root="../local/bids"):
-    bids_path = BIDSPath(subject=subject_id, task="P3", session="P3",
-                         datatype='eeg', suffix='eeg',
-                         root=bids_root)
-
-    raw = read_raw_bids(bids_path)
-    read_annotations_core(bids_path, raw)
-    # raw.pick_channels(["Cz"])#["Pz","Fz","Cz"])
-    raw.load_data()
-    raw.set_montage('standard_1020', match_case=False)
-
-    evts, evts_dict = mne.events_from_annotations(raw)
-    wanted_keys = [e for e in evts_dict.keys() if "response" in e]
-    evts_dict_stim = dict((k, evts_dict[k]) for k in wanted_keys if k in evts_dict)
-    epochs = mne.Epochs(raw, evts, evts_dict_stim, tmin=-1, tmax=2)
-    return epochs
-
-
-def get_classification_dataset(subject=1, typeInt=4):
-    # TypeInt:
-    # Task 1 (open and close left or right fist)
-    # Task 2 (imagine opening and closing left or right fist)
-    # Task 3 (open and close both fists or both feet)
-    # Task 4 (imagine opening and closing both fists or both feet)
-    assert (typeInt >= 1)
-    assert (typeInt <= 4)
-    from mne.io import concatenate_raws, read_raw_edf
-    from mne.datasets import eegbci
-    tmin, tmax = -1., 4.
-    runs = [3, 7, 11]
-    runs = [r + typeInt - 1 for r in runs]
-    print("loading subject {} with runs {}".format(subject, runs))
-    if typeInt <= 1:
-        event_id = dict(left=2, right=3)
-    else:
-        event_id = dict(hands=2, feet=3)
-
-    raw_fnames = eegbci.load_data(subject, runs)
-    raws = [read_raw_edf(f, preload=True) for f in raw_fnames]
-    raw = concatenate_raws(raws)
-
-    raw.filter(7., 30., fir_design='firwin', skip_by_annotation='edge')
-
-    eegbci.standardize(raw)  # set channel names
-    montage = mne.channels.make_standard_montage('standard_1005')
-    raw.set_montage(montage)
-    raw.rename_channels(lambda x: x.strip('.'))
-    events, _ = mne.events_from_annotations(raw, event_id=dict(T1=2, T2=3))
-
-    picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
-                           exclude='bads')
-
-    # Read epochs (train will be done only between 1 and 2s)
-    # Testing will be done with a running classifier
-    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
-                        baseline=None, preload=True)
-    return (epochs)
-
-
-def ex8_simulateData(width=40, n_subjects=15, signal_mean=100, noise_between=30, noise_within=10, smooth_sd=4,
-                     rng_seed=43):
-    # adapted and extended from https://mne.tools/dev/auto_tutorials/discussions/plot_background_statistics.html#sphx-glr-auto-tutorials-discussions-plot-background-statistics-py
-    rng = np.random.RandomState(rng_seed)
-    # For each "subject", make a smoothed noisy signal with a centered peak
-
-    X = noise_within * rng.randn(n_subjects, width, width)
-    # Add three signals
-    X[:, width // 6 * 2, width // 6 * 2] -= signal_mean / 3 * 3 + rng.randn(n_subjects) * noise_between
-    X[:, width // 6 * 4, width // 6 * 4] += signal_mean / 3 * 2 + rng.randn(n_subjects) * noise_between
-    X[:, width // 6 * 5, width // 6 * 5] += signal_mean / 3 * 2 + rng.randn(n_subjects) * noise_between
-    # Spatially smooth with a 2D Gaussian kernel
-    size = width // 2 - 1
-    gaussian = np.exp(-(np.arange(-size, size + 1) ** 2 / float(smooth_sd ** 2)))
-    for si in range(X.shape[0]):
-        for ri in range(X.shape[1]):
-            X[si, ri, :] = np.convolve(X[si, ri, :], gaussian, 'same')
-        for ci in range(X.shape[2]):
-            X[si, :, ci] = np.convolve(X[si, :, ci], gaussian, 'same')
-    # X += 10 * rng.randn(n_subjects, width, width)
-    return X
-
-
-def stc_plot2img(h, title="SourceEstimate", closeAfterwards=False, crop=True):
-    h.add_text(0.1, 0.9, title, 'title', font_size=16)
-    screenshot = h.screenshot()
-    if closeAfterwards:
-        h.close()
-
-    if crop:
-        nonwhite_pix = (screenshot != 255).any(-1)
-        nonwhite_row = nonwhite_pix.any(1)
-        nonwhite_col = nonwhite_pix.any(0)
-        screenshot = screenshot[nonwhite_row][:, nonwhite_col]
-    return screenshot

utils/ccs_eeg_utils_reduced.py

@@ -0,0 +1,62 @@
+import os
+from mne_bids.read import _from_tsv, _drop
+import mne
+import numpy as np
+
+"""
+The ccs_eeg_utils.py file from the lecture, but reduced to the read_annotations_core method needed for the project
+"""
+
+
+def read_annotations_core(bids_path, raw):
+    tsv = os.path.join(bids_path.directory, bids_path.update(suffix="events", extension=".tsv").basename)
+    _handle_events_reading_core(tsv, raw)
+
+
+def _handle_events_reading_core(events_fname, raw):
+    """Read associated events.tsv and populate raw.
+    Handle onset, duration, and description of each event.
+    """
+    events_dict = _from_tsv(events_fname)
+
+    if ('value' in events_dict) and ('trial_type' in events_dict):
+        events_dict = _drop(events_dict, 'n/a', 'trial_type')
+        events_dict = _drop(events_dict, 'n/a', 'value')
+
+        descriptions = np.asarray([a + ':' + b for a, b in zip(events_dict["trial_type"], events_dict["value"])],
+                                  dtype=str)
+
+        # Get the descriptions of the events
+    elif 'trial_type' in events_dict:
+
+        # Drop events unrelated to a trial type
+        events_dict = _drop(events_dict, 'n/a', 'trial_type')
+        descriptions = np.asarray(events_dict['trial_type'], dtype=str)
+
+    # If we don't have a proper description of the events, perhaps we have
+    # at least an event value?
+    elif 'value' in events_dict:
+        # Drop events unrelated to value
+        events_dict = _drop(events_dict, 'n/a', 'value')
+        descriptions = np.asarray(events_dict['value'], dtype=str)
+    # Worst case, we go with 'n/a' for all events
+    else:
+        descriptions = 'n/a'
+    # Deal with "n/a" strings before converting to float
+    ons = [np.nan if on == 'n/a' else on for on in events_dict['onset']]
+    dus = [0 if du == 'n/a' else du for du in events_dict['duration']]
+    onsets = np.asarray(ons, dtype=float)
+    durations = np.asarray(dus, dtype=float)
+    # Keep only events where onset is known
+    good_events_idx = ~np.isnan(onsets)
+    onsets = onsets[good_events_idx]
+    durations = durations[good_events_idx]
+    descriptions = descriptions[good_events_idx]
+    del good_events_idx
+    # Add Events to raw as annotations
+    annot_from_events = mne.Annotations(onset=onsets,
+                                        duration=durations,
+                                        description=descriptions,
+                                        orig_time=None)
+    raw.set_annotations(annot_from_events)
+    return raw