コード例 #1
0
def main(to_file):

    layout_fname = "gsn_129.lout"
    layout_path = "/home/zairex/Code/cibr/materials/"
    layout = mne.channels.read_layout(layout_fname, layout_path)

    raw = cli_raws()[0]

    raw.filter(l_freq=1, h_freq=100)

    ica = mne.preprocessing.ICA(n_components=0.95, method="fastica")
    ica.fit(raw)

    sources = ica.get_sources(raw)

    # alter amplitudes to get better plot
    for source in sources._data:
        for idx, amplitude in enumerate(source):
            source[idx] = amplitude / 5000.0

    ica.plot_components(layout=layout, show=False)
    sources.plot()

    indices = raw_input(
        "Please enter indices (starts from one) of "
        "ICA components to be zeroed out "
        "(separated with spaces, empty for none): "
    )
    if indices:
        indices = map(int, indices.split(" "))
        indices = [i - 1 for i in indices]

        # project out selected ica components
        ica.apply(raw, exclude=indices)

    raw.plot()

    to_be_saved = raw_input("Do you want to save (y, n)? ")

    if to_be_saved == "y":
        raw.save(to_file, overwrite=True)
コード例 #2
0
ファイル: tfr_analysis.py プロジェクト: Teekuningas/signals
import sys

import mne
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches

from scipy.ndimage.filters import convolve

from lib.load import cli_raws
from lib.load import get_raw

# get data
raws = cli_raws()

if not raws:
    raws = [get_raw('KH003', 'eoec'), get_raw('KH003', 'med')]

for raw in raws:
    picks = mne.pick_types(raw.info, eeg=True, meg=True)
    raw.drop_channels([name for idx, name in enumerate(raw.info['ch_names'])
                       if idx not in picks])

raw = raws[0]
raw.append(raws[1:])

# set triggers
triggers = [95000, 205000]

# get threshold parameter
args = sys.argv
コード例 #3
0
import sys

import numpy as np
import matplotlib.pyplot as plt

import mne

from lib.load import cli_raws

PLOTS = 6

raw = cli_raws()[0]

raw.filter(l_freq=1, h_freq=100)

ica = mne.preprocessing.ICA(n_components=0.9, method='fastica')
ica.fit(raw)

sources = ica.get_sources(raw)

# alter amplitudes to get better plot
for source in sources._data:
    for idx, amplitude in enumerate(source):
	source[idx] = amplitude / 5000.0

sources.plot()

index = raw_input("Please enter index of blink channel: ")

index = int(index) - 1