detrend_correction (#9)

.gitignore

@@ -1,4 +1,5 @@
 .idea/*
+/idea
 /src
 /.pytest_*
 docs/generated

docs/whats_new.rst

@@ -21,6 +21,7 @@ Current
 Changelog
 ~~~~~~~~~
 
+- Included a boolean which indicates if detrend should be done internally or not for the use of find_noisy_channels in reference.py. `Yorguin Mantilla`_
 - Robust average referencing + tests by  `Victor Xiang`_
 - Removing trend in the EEG data by high pass filtering and local linear regression + tests `Aamna Lawrence`_
 - Finding noisy channels with comparable output to Matlab +tests-including test for ransac `Aamna Lawrence`_
@@ -30,6 +31,12 @@ Changelog
 - Finding the reason for the difference between the Matlab and Pyprep’s output- Probably minor differences in the filter functions and also rounding done by functions like quantile `Victor Xiang`_  and `Aamna Lawrence`_
 - Overall debugging `Victor Xiang`_  and `Aamna Lawrence`_
 
+
+Bug
+~~~
+
+- In find_noisy_channels the signal detrend is accidentally undone which destabilized the RANSAC. The detrend is now done over the NoisyChannels.raw_mne object to avoid this bug and to force that any signal in there is detrended like in matlab's prep. By `Yorguin Mantilla`_ (`#29 <https://github.com/sappelhoff/pyprep/pull/9>`_)
+
 API
 ~~~
 
@@ -75,8 +82,10 @@ People who contributed to this software (in alphabetical order)
 * Aamna Lawrence
 * Adam Li
 * Victor Xiang
+* Yorguin Mantilla
 
 .. _Stefan Appelhoff: http://stefanappelhoff.com/
 .. _Aamna Lawrence: https://github.com/AamnaLawrence
 .. _Adam Li: https://github.com/adam2392/
 .. _Victor Xiang: https://github.com/Nick3151
+.. _Yorguin Mantilla: https://github.com/yjmantilla

pyprep/find_noisy_channels.py

@@ -24,15 +24,19 @@ class NoisyChannels:
 
     """
 
-    def __init__(self, raw):
+    def __init__(self, raw, do_detrend=True):
         """Initialize the class."""
         # Make sure that we got an MNE object
         assert isinstance(raw, mne.io.BaseRaw)
 
         self.raw_mne = raw.copy()
         self.sample_rate = raw.info["sfreq"]
+        if do_detrend:
+            self.raw_mne._data = removeTrend(
+                self.raw_mne.get_data(), sample_rate=self.sample_rate
+            )
+
         self.EEGData = self.raw_mne.get_data(picks="eeg")
-        self.EEGData = removeTrend(self.EEGData, sample_rate=self.sample_rate)
         self.EEGData_beforeFilt = self.EEGData
         self.ch_names_original = np.asarray(raw.info["ch_names"])
         self.n_chans_original = len(self.ch_names_original)
@@ -237,7 +241,7 @@ def find_bad_by_hfnoise(self, HF_zscore_threshold=5.0):
         return None
 
     def find_bad_by_correlation(
-        self, correlation_secs=1.0, correlation_threshold=0.4, frac_bad=0.1
+        self, correlation_secs=1.0, correlation_threshold=0.4, frac_bad=0.01
     ):
         """Find correlation between the low frequency components of the EEG below 50 Hz.
 

pyprep/reference.py

@@ -131,7 +131,7 @@ def robust_reference(self):
         raw._data = removeTrend(raw.get_data(), sample_rate=self.sfreq)
 
         # Determine unusable channels and remove them from the reference channels
-        noisy_detector = NoisyChannels(raw)
+        noisy_detector = NoisyChannels(raw, do_detrend=False)
         noisy_detector.find_all_bads(ransac=self.ransac)
         self.noisy_channels_original = {
             "bad_by_nan": noisy_detector.bad_by_nan,