Hilbert Detector¶
[1]:
# first let's load in all our packages
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
import re
import pandas as pd
from mne_bids import (read_raw_bids, BIDSPath,
get_entity_vals, get_datatypes,
make_report)
from mne_bids.stats import count_events
import mne
from mne import make_ad_hoc_cov
basepath = os.path.join(os.getcwd(), "../..")
sys.path.append(basepath)
from mne_hfo import HilbertDetector
[2]:
%%capture
# this may change depending on where you store the data
root = "C:/Users/patri/Dropbox/fedele_hfo_data"
subjects = get_entity_vals(root, 'subject')
sessions = get_entity_vals(root, 'session')
subjectID = subjects[0]
sessionID = sessions[0]
bids_path = BIDSPath(subject=subjectID, session=sessionID,
datatype='ieeg',
suffix='ieeg',
extension='.vhdr', root=root)
# get first matching dataset
fpath = bids_path.match()[0]
# load dataset into mne Raw object
extra_params = dict(preload=True)
raw = read_raw_bids(fpath, extra_params)
[3]:
def convert_to_bipolar(raw, drop_originals=True):
original_ch_names = raw.ch_names
ch_names_sorted = sorted(original_ch_names)
ch_pairs = []
for first, second in zip(ch_names_sorted, ch_names_sorted[1:]):
firstName = re.sub(r'[0-9]+', '', first)
secondName = re.sub(r'[0-9]+', '', second)
if firstName == secondName:
ch_pairs.append((first,second))
for ch_pair in ch_pairs:
raw = mne.set_bipolar_reference(raw, ch_pair[0], ch_pair[1], drop_refs=False)
if drop_originals:
raw = raw.drop_channels(original_ch_names)
return raw
[4]:
%%capture
raw = convert_to_bipolar(raw)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-f6e3da9a949b> in <module>
2 info = raw.info
3 sfreq = info['sfreq']
----> 4 data = raw.get_data(start=0, stop=30*sfreq)
5 raw = mne.io.RawArray(data, info=info)
<decorator-gen-170> in get_data(self, picks, start, stop, reject_by_annotation, return_times, verbose)
~\.virtualenvs\mne-hfo-tjBZjdVA\lib\site-packages\mne\io\base.py in get_data(self, picks, start, stop, reject_by_annotation, return_times, verbose)
823 stop = min(self.n_times if stop is None else stop, self.n_times)
824 if len(self.annotations) == 0 or reject_by_annotation is None:
--> 825 data, times = self[picks, start:stop]
826 return (data, times) if return_times else data
827 _check_option('reject_by_annotation', reject_by_annotation.lower(),
~\.virtualenvs\mne-hfo-tjBZjdVA\lib\site-packages\mne\io\base.py in __getitem__(self, item)
770 sel, start, stop = self._parse_get_set_params(item)
771 if self.preload:
--> 772 data = self._data[sel, start:stop]
773 else:
774 data = self._read_segment(start=start, stop=stop, sel=sel,
TypeError: slice indices must be integers or None or have an __index__ method
[7]:
info = raw.info
sfreq = info['sfreq']
data = raw.get_data(start=0, stop=int(30*sfreq))
raw = mne.io.RawArray(data, info=info)
Creating RawArray with float64 data, n_channels=43, n_times=60000
Range : 0 ... 59999 = 0.000 ... 30.000 secs
Ready.
[8]:
kwargs = {
'band_method': 'log',
'filter_band': (80, 250), # (l_freq, h_freq)
'threshold': 3, # Number of st. deviations
'hfo_name': "ripple",
'n_jobs': 1,
}
hil_detector = HilbertDetector(**kwargs)
[9]:
raw.n_times
[9]:
60000
[10]:
# perform the fits
hil_detector = hil_detector.fit(raw)
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[12]:
hil_detector.df_
[12]:
| onset | duration | label | channels | sample | |
|---|---|---|---|---|---|
| 0 | 10.4095 | 0.6320 | ripple | AHR1-AHR2 | 20819.0 |
| 1 | 14.2345 | 0.2995 | ripple | AHR1-AHR2 | 28469.0 |
| 2 | 16.0610 | 0.3110 | ripple | AHR1-AHR2 | 32122.0 |
| 3 | 23.8995 | 0.4905 | ripple | AHR1-AHR2 | 47799.0 |
| 4 | 10.4225 | 0.5130 | ripple | AHR2-AHR3 | 20845.0 |
| ... | ... | ... | ... | ... | ... |
| 69 | 16.0450 | 0.5025 | ripple | PHR4-PHR5 | 32090.0 |
| 70 | 23.9560 | 0.5325 | ripple | PHR4-PHR5 | 47912.0 |
| 71 | 6.4010 | 0.1840 | ripple | PHR5-PHR6 | 12802.0 |
| 72 | 1.4250 | 0.1630 | ripple | PHR7-PHR8 | 2850.0 |
| 73 | 28.2535 | 0.3480 | ripple | PHR7-PHR8 | 56507.0 |
74 rows × 5 columns
[ ]: