diff --git a/bin/mne_clean_eog_ecg.py b/bin/mne_clean_eog_ecg.py
index 01d3ca36d20..f12e73b3e13 100755
--- a/bin/mne_clean_eog_ecg.py
+++ b/bin/mne_clean_eog_ecg.py
@@ -56,7 +56,7 @@ def clean_ecg_eog(in_fif_fname, out_fif_fname=None, eog=True, ecg=True,
     print 'Implementing ECG and EOG artifact rejection on data'
 
     if ecg:
-        ecg_events, _, _  = mne.artifacts.find_ecg_events(raw_in)
+        ecg_events, _, _  = mne.preprocessing.find_ecg_events(raw_in)
         print "Writing ECG events in %s" % ecg_event_fname
         mne.write_events(ecg_event_fname, ecg_events)
 
@@ -73,7 +73,7 @@ def clean_ecg_eog(in_fif_fname, out_fif_fname=None, eog=True, ecg=True,
             print "Error while running : %s" % command
 
     if eog:
-        eog_events = mne.artifacts.find_eog_events(raw_in)
+        eog_events = mne.preprocessing.find_eog_events(raw_in)
         print "Writing EOG events in %s" % eog_event_fname
         mne.write_events(eog_event_fname, eog_events)
 
diff --git a/doc/source/python_reference.rst b/doc/source/python_reference.rst
index 120278ef829..f04379eb934 100644
--- a/doc/source/python_reference.rst
+++ b/doc/source/python_reference.rst
@@ -22,7 +22,7 @@ Classes reference
    mne.SourceEstimate
    mne.Label
    mne.BiHemiLabel
-   mne.artifacts.ICA
+   mne.preprocessing.ICA
 
 Functions reference
 ===================
@@ -181,16 +181,18 @@ Functions reference
    plot_topo_image_epochs
    mne_analyze_colormap
 
-.. automodule:: mne.artifacts
+.. automodule:: mne.preprocessing
  :no-members:
  :no-inherited-members:
 
-.. currentmodule:: mne.artifacts
+.. currentmodule:: mne.preprocessing
 
 .. autosummary::
    :toctree: generated/
    :template: function.rst
 
+   compute_proj_ecg
+   compute_proj_eog
    find_eog_events
    find_ecg_events
    ica_find_eog_events
diff --git a/doc/source/whats_new.rst b/doc/source/whats_new.rst
index 30a41fa963b..7ec92372414 100644
--- a/doc/source/whats_new.rst
+++ b/doc/source/whats_new.rst
@@ -73,6 +73,8 @@ API
 
    - Use raw.time_as_index instead of time_to_index (still works but is deprecated).
 
+   - The artifacts module (mne.artifacts) is now merged into mne.preprocessing
+
 .. _changes_0_4:
 
 Version 0.4
diff --git a/mne/artifacts/__init__.py b/mne/artifacts/__init__.py
index e4a6a8127ca..a5c1698dd72 100644
--- a/mne/artifacts/__init__.py
+++ b/mne/artifacts/__init__.py
@@ -1,6 +1,11 @@
 """Artifacts finding/correction related functions
 """
 
-from .eog import find_eog_events
-from .ecg import find_ecg_events
-from .ica import ICA, ica_find_eog_events, ica_find_ecg_events, score_funcs
\ No newline at end of file
+from ..utils import deprecated
+from ..preprocessing import find_eog_events, find_ecg_events
+
+_preprocessing = ('The artifacts module will no longer be supported in v0.6.'
+                  ' Please use the preprocessing module instead.')
+
+find_eog_events = deprecated(_preprocessing)(find_eog_events)
+find_ecg_events = deprecated(_preprocessing)(find_ecg_events)
diff --git a/mne/artifacts/ecg.py b/mne/artifacts/ecg.py
deleted file mode 100644
index ee07eba7db2..00000000000
--- a/mne/artifacts/ecg.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import numpy as np
-
-import logging
-logger = logging.getLogger('mne')
-
-from .. import fiff, verbose
-from ..filter import band_pass_filter
-from ..utils import deprecated
-
-_preprocessing = ('The artifacts module will no longer be supported.'
-                  ' Please use the preprocessing module instead.')
-
-
-@deprecated(_preprocessing)
-def qrs_detector(sfreq, ecg, thresh_value=0.6, levels=2.5, n_thresh=3,
-                 l_freq=5, h_freq=35, tstart=0):
-    """Detect QRS component in ECG channels.
-
-    QRS is the main wave on the heart beat.
-
-    Parameters
-    ----------
-    sfreq : float
-        Sampling rate
-    ecg : array
-        ECG signal
-    thresh_value: float
-        qrs detection threshold
-    levels: float
-        number of std from mean to include for detection
-    n_thresh: int
-        max number of crossings
-    l_freq: float
-        Low pass frequency
-    h_freq: float
-        High pass frequency
-    tstart: float
-        Start detection after tstart seconds.
-
-    Returns
-    -------
-    events : array
-        Indices of ECG peaks
-    """
-    win_size = round((60.0 * sfreq) / 120.0)
-
-    filtecg = band_pass_filter(ecg, sfreq, l_freq, h_freq)
-
-    absecg = np.abs(filtecg)
-    init = int(sfreq)
-
-    n_samples_start = int(init * tstart)
-    absecg = absecg[n_samples_start:]
-
-    n_points = len(absecg)
-
-    maxpt = np.empty(3)
-    maxpt[0] = np.max(absecg[:init])
-    maxpt[1] = np.max(absecg[init:init * 2])
-    maxpt[2] = np.max(absecg[init * 2:init * 3])
-
-    init_max = np.mean(maxpt)
-
-    thresh1 = init_max * thresh_value
-
-    numcross = []
-    time = []
-    rms = []
-    i = 0
-    while i < (n_points - win_size):
-        window = absecg[i:i + win_size]
-        if window[0] > thresh1:
-            maxTime = np.argmax(window)
-            time.append(i + maxTime)
-            numcross.append(np.sum(np.diff((window > thresh1).astype(np.int)
-                                            == 1)))
-            rms.append(np.sqrt(np.mean(window ** 2)))
-            i += win_size
-        else:
-            i += 1
-
-    time = np.array(time)
-    rms_mean = np.mean(rms)
-    rms_std = np.std(rms)
-    rms_thresh = rms_mean + (rms_std * levels)
-    b = np.where(rms < rms_thresh)[0]
-    a = np.array(numcross)[b]
-    clean_events = time[b[a < n_thresh]]
-
-    clean_events += n_samples_start
-
-    return clean_events
-
-
-@deprecated(_preprocessing)
-@verbose
-def find_ecg_events(raw, event_id=999, ch_name=None, tstart=0.0,
-                    l_freq=5, h_freq=35, qrs_threshold=0.6, verbose=None):
-    """Find ECG peaks
-
-    Parameters
-    ----------
-    raw : instance of Raw
-        The raw data
-    event_id : int
-        The index to assign to found events
-    ch_name : str
-        The name of the channel to use for ECG peak detection.
-        The argument is mandatory if the dataset contains no ECG
-        channels.
-    tstart : float
-        Start detection after tstart seconds. Useful when beginning
-        of run is noisy.
-    l_freq : float
-        Low pass frequency.
-    h_freq : float
-        High pass frequency.
-    qrs_threshold : float
-        Between 0 and 1. qrs detection threshold.
-    verbose : bool, str, int, or None
-        If not None, override default verbose level (see mne.verbose).
-
-    Returns
-    -------
-    ecg_events : array
-        Events.
-    ch_ECG : string
-        Name of channel used.
-    average_pulse : float
-        Estimated average pulse.
-    """
-    info = raw.info
-
-    # Geting ECG Channel
-    if ch_name is None:
-        ch_ECG = fiff.pick_types(info, meg=False, eeg=False, stim=False,
-                                 eog=False, ecg=True, emg=False)
-    else:
-        ch_ECG = fiff.pick_channels(raw.ch_names, include=[ch_name])
-        if len(ch_ECG) == 0:
-            raise ValueError('%s not in channel list (%s)' %
-                              (ch_name, raw.ch_names))
-
-    if len(ch_ECG) == 0 and ch_name is None:
-        raise Exception('No ECG channel found. Please specify ch_name '
-                        'parameter e.g. MEG 1531')
-
-    assert len(ch_ECG) == 1
-
-    logger.info('Using channel %s to identify heart beats'
-                % raw.ch_names[ch_ECG[0]])
-
-    ecg, times = raw[ch_ECG, :]
-
-    # detecting QRS and generating event file
-    ecg_events = qrs_detector(info['sfreq'], ecg.ravel(), tstart=tstart,
-                              thresh_value=qrs_threshold, l_freq=l_freq,
-                              h_freq=h_freq)
-
-    n_events = len(ecg_events)
-    average_pulse = n_events * 60.0 / (times[-1] - times[0])
-    logger.info("Number of ECG events detected : %d (average pulse %d / "
-                "min.)" % (n_events, average_pulse))
-
-    ecg_events = np.c_[ecg_events + raw.first_samp, np.zeros(n_events),
-                       event_id * np.ones(n_events)]
-    return ecg_events, ch_ECG, average_pulse
diff --git a/mne/artifacts/eog.py b/mne/artifacts/eog.py
deleted file mode 100644
index ba2d4e47f8c..00000000000
--- a/mne/artifacts/eog.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import numpy as np
-
-import logging
-logger = logging.getLogger('mne')
-
-from .peak_finder import peak_finder
-from .. import fiff, verbose
-from ..filter import band_pass_filter
-from .. utils import deprecated
-
-
-_preprocessing = ('The artifacts module will no longer be supported.'
-                  ' Please use the preprocessing module instead.')
-
-
-@deprecated(_preprocessing)
-@verbose
-def find_eog_events(raw, event_id=998, l_freq=1, h_freq=10, verbose=None):
-    """Locate EOG artifacts
-
-    Parameters
-    ----------
-    raw : instance of Raw
-        The raw data.
-    event_id : int
-        The index to assign to found events.
-    low_pass : float
-        Low pass frequency.
-    high_pass : float
-        High pass frequency.
-    verbose : bool, str, int, or None
-        If not None, override default verbose level (see mne.verbose).
-
-    Returns
-    -------
-    eog_events : array
-        Events.
-    """
-    info = raw.info
-
-    # Geting EOG Channel
-    ch_EOG = fiff.pick_types(info, meg=False, eeg=False, stim=False,
-                             eog=True, ecg=False, emg=False)
-
-    if len(ch_EOG) == 0:
-        logger.info('No EOG channels found')
-        logger.info('Trying with EEG 061 and EEG 062')
-        ch_EOG = fiff.pick_channels(raw.ch_names,
-                                        include=['EEG 061', 'EEG 062'])
-        if len(ch_EOG) != 2:
-            raise ValueError('EEG 61 or EEG 62 channel not found !!')
-
-    logger.info('EOG channel index for this subject is: %s' % ch_EOG)
-
-    eog, _ = raw[ch_EOG, :]
-
-    eog_events = _find_eog_events(eog, event_id=event_id, l_freq=l_freq,
-                                  h_freq=h_freq,
-                                  sampling_rate=raw.info['sfreq'],
-                                  first_samp=raw.first_samp)
-
-    return eog_events
-
-
-@deprecated(_preprocessing)
-def _find_eog_events(eog, event_id, l_freq, h_freq, sampling_rate, first_samp):
-    """Helper function"""
-
-    logger.info('Filtering the data to remove DC offset to help '
-                'distinguish blinks from saccades')
-
-    # filtering to remove dc offset so that we know which is blink and saccades
-    filteog = np.array([band_pass_filter(x, sampling_rate, 2, 45) for x in eog])
-    temp = np.sqrt(np.sum(filteog ** 2, axis=1))
-
-    indexmax = np.argmax(temp)
-
-    # easier to detect peaks with filtering.
-    filteog = band_pass_filter(eog[indexmax], sampling_rate, l_freq, h_freq)
-
-    # detecting eog blinks and generating event file
-
-    logger.info('Now detecting blinks and generating corresponding events')
-
-    temp = filteog - np.mean(filteog)
-    if np.abs(np.max(temp)) > np.abs(np.min(temp)):
-        eog_events, _ = peak_finder(filteog, extrema=1)
-    else:
-        eog_events, _ = peak_finder(filteog, extrema=-1)
-
-    n_events = len(eog_events)
-    logger.info("Number of EOG events detected : %d" % n_events)
-    eog_events = np.c_[eog_events + first_samp, np.zeros(n_events),
-                       event_id * np.ones(n_events)]
-
-    return eog_events
diff --git a/mne/artifacts/ica.py b/mne/artifacts/ica.py
deleted file mode 100644
index 8ed8573b06f..00000000000
--- a/mne/artifacts/ica.py
+++ /dev/null
@@ -1,958 +0,0 @@
-# Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
-#          Matti Hamalainen <msh@nmr.mgh.harvard.edu>
-#          Denis Engemann <d.engemann@fz-juelich.de>
-#
-# License: BSD (3-clause)
-
-from copy import deepcopy
-import inspect
-import warnings
-from inspect import getargspec, isfunction
-
-import logging
-logger = logging.getLogger('mne')
-
-import numpy as np
-from scipy import stats
-from scipy.spatial import distance
-from scipy import linalg
-
-from .ecg import qrs_detector
-from .eog import _find_eog_events
-
-from ..cov import compute_whitener
-from ..fiff import pick_types, pick_channels
-from ..fiff.constants import Bunch, FIFF
-from ..viz import plot_ica_panel
-from .. import verbose
-from .. utils import deprecated
-
-
-def _make_xy_sfunc(func, ndim_output=False):
-    """Helper Function"""
-    if ndim_output:
-        sfunc = lambda x, y: np.array([func(a, y.ravel()) for a in x])[:, 0]
-    else:
-        sfunc = lambda x, y: np.array([func(a, y.ravel()) for a in x])
-    sfunc.__name__ = '.'.join(['score_func', func.__module__, func.__name__])
-    sfunc.__doc__ = func.__doc__
-    return sfunc
-
-# makes score funcs attr accessible for users
-score_funcs = Bunch()
-
-xy_arg_dist_funcs = [(n, f) for n, f in vars(distance).items() if isfunction(f)
-                     and not n.startswith('_')]
-
-xy_arg_stats_funcs = [(n, f) for n, f in vars(stats).items() if isfunction(f)
-                      and not n.startswith('_')]
-
-score_funcs.update(dict((n, _make_xy_sfunc(f)) for n, f in xy_arg_dist_funcs
-                   if getargspec(f).args == ['u', 'v']))
-
-score_funcs.update(dict((n, _make_xy_sfunc(f, ndim_output=True))
-                   for n, f in xy_arg_stats_funcs
-                   if getargspec(f).args == ['x', 'y']))
-
-
-__all__ = ['ICA', 'ica_find_ecg_events', 'ica_find_eog_events', 'score_funcs']
-
-_preprocessing = ('The artifacts module will no longer be supported.'
-                  ' Please use the preprocessing module instead.')
-
-
-class ICA(object):
-    """M/EEG signal decomposition using Independent Component Analysis (ICA)
-
-    This object can be used to estimate ICA components and then
-    remove some from Raw or Epochs for data exploration or artifact
-    correction.
-
-    Parameters
-    ----------
-    n_components : int | float | None
-        The number of components used for ICA decomposition. If int, it must be
-        smaller then max_n_components. If None, all PCA components will be
-        used. If float between 0 and 1 components can will be selected by the
-        cumulative percentage of explained variance.
-    max_n_components : int | None
-        The number of components used for PCA decomposition. If None, no
-        dimension reduction will be applied and max_n_components will equal
-        the number of channels supplied on decomposing data.
-    noise_cov : None | instance of mne.cov.Covariance
-        Noise covariance used for whitening. If None, channels are just
-        z-scored.
-    random_state : None | int | instance of np.random.RandomState
-        np.random.RandomState to initialize the FastICA estimation.
-        As the estimation is non-deterministic it can be useful to
-        fix the seed to have reproducible results.
-    algorithm : {'parallel', 'deflation'}
-        Apply parallel or deflational algorithm for FastICA
-    fun : string or function, optional. Default: 'logcosh'
-        The functional form of the G function used in the
-        approximation to neg-entropy. Could be either 'logcosh', 'exp',
-        or 'cube'.
-        You can also provide your own function. It should return a tuple
-        containing the value of the function, and of its derivative, in the
-        point.
-    fun_args: dictionary, optional
-        Arguments to send to the functional form.
-        If empty and if fun='logcosh', fun_args will take value
-        {'alpha' : 1.0}
-    verbose : bool, str, int, or None
-        If not None, override default verbose level (see mne.verbose).
-
-    Attributes
-    ----------
-    last_fit : str
-        Flag informing about which type was last fit.
-    ch_names : list-like
-        Channel names resulting from initial picking.
-    n_components : int
-        The number of components used for ICA decomposition.
-    max_n_components : int
-        The number of PCA dimensions computed.
-    verbose : bool, str, int, or None
-        See above.
-    """
-    @deprecated(_preprocessing)
-    @verbose
-    def __init__(self, n_components, max_n_components=100, noise_cov=None,
-                 random_state=None, algorithm='parallel', fun='logcosh',
-                 fun_args=None, verbose=None):
-        try:
-            from sklearn.decomposition import FastICA  # to avoid strong dep.
-        except ImportError:
-            raise Exception('the scikit-learn package is missing and '
-                            'required for ICA')
-        self.noise_cov = noise_cov
-
-        # sklearn < 0.11 does not support random_state argument for FastICA
-        kwargs = {'algorithm': algorithm, 'fun': fun, 'fun_args': fun_args}
-
-        if random_state is not None:
-            aspec = inspect.getargspec(FastICA.__init__)
-            if 'random_state' not in aspec.args:
-                warnings.warn('random_state argument ignored, update '
-                              'scikit-learn to version 0.11 or newer')
-            else:
-                kwargs['random_state'] = random_state
-
-        if max_n_components is not None and n_components > max_n_components:
-            raise ValueError('n_components must be smaller than '
-                             'max_n_components')
-
-        if isinstance(n_components, float):
-            if not 0 < n_components <= 1:
-                raise ValueError('For selecting ICA components by the '
-                                 'explained variance of PCA components the'
-                                 ' float value must be between 0.0 and 1.0 ')
-            self._explained_var = n_components
-            logger.info('Selecting pca_components via explained variance.')
-        else:
-            self._explained_var = 1.1
-            logger.info('Selecting pca_components directly.')
-
-        self._ica = FastICA(**kwargs)
-        self.current_fit = 'unfitted'
-        self.verbose = verbose
-        self.n_components = n_components
-        self.max_n_components = max_n_components
-        self.ch_names = None
-        self._mixing = None
-
-    def __repr__(self):
-        s = 'ICA '
-        if self.current_fit == 'unfitted':
-            msg = '(no'
-        elif self.current_fit == 'raw':
-            msg = '(raw data'
-        else:
-            msg = '(epochs'
-        msg += ' decomposition, '
-
-        s += msg + ('%s components' % str(self.n_components) if
-               self.n_components else 'no dimension reduction') + ')'
-
-        return s
-
-    @deprecated(_preprocessing)
-    @verbose
-    def decompose_raw(self, raw, picks=None, start=None, stop=None,
-                      verbose=None):
-        """Run the ICA decomposition on raw data
-
-        Parameters
-        ----------
-        raw : instance of mne.fiff.Raw
-            Raw measurements to be decomposed.
-        picks : array-like
-            Channels to be included. This selection remains throughout the
-            initialized ICA session. If None only good data channels are used.
-        start : int
-            First sample to include (first is 0). If omitted, defaults to the
-            first sample in data.
-        stop : int
-            First sample to not include. If omitted, data is included to the
-            end.
-        verbose : bool, str, int, or None
-            If not None, override default verbose level (see mne.verbose).
-            Defaults to self.verbose.
-
-        Returns
-        -------
-        self : instance of ICA
-            Returns the modified instance.
-        """
-        if self.current_fit != 'unfitted':
-            raise RuntimeError('ICA decomposition has already been fitted. '
-                               'Please start a new ICA session.')
-
-        logger.info('Computing signal decomposition on raw data. '
-                    'Please be patient, this may take some time')
-
-        if picks is None:  # just use good data channels
-            picks = pick_types(raw.info, meg=True, eeg=True, eog=False,
-                               ecg=False, misc=False, stim=False,
-                               exclude=raw.info['bads'])
-
-        if self.max_n_components is None:
-            self.max_n_components = len(picks)
-            logger.info('Inferring max_n_components from picks.')
-
-        self.ch_names = [raw.ch_names[k] for k in picks]
-
-        data, self._pre_whitener = self._pre_whiten(raw[picks, start:stop][0],
-                                                   raw.info, picks)
-
-        to_ica, self._pca = self._prepare_pca(data, self.max_n_components)
-
-        self._ica.fit(to_ica)
-        self._mixing = self._ica.get_mixing_matrix().T
-        self.current_fit = 'raw'
-
-        return self
-
-    @deprecated(_preprocessing)
-    @verbose
-    def decompose_epochs(self, epochs, picks=None, verbose=None):
-        """Run the ICA decomposition on epochs
-
-        Parameters
-        ----------
-        epochs : instance of Epochs
-            The epochs. The ICA is estimated on the concatenated epochs.
-        picks : array-like
-            Channels to be included relative to the channels already picked on
-            epochs-initialization. This selection remains throughout the
-            initialized ICA session.
-        verbose : bool, str, int, or None
-            If not None, override default verbose level (see mne.verbose).
-            Defaults to self.verbose.
-
-        Returns
-        -------
-        self : instance of ICA
-            Returns the modified instance.
-        """
-        if self.current_fit != 'unfitted':
-            raise RuntimeError('ICA decomposition has already been fitted. '
-                               'Please start a new ICA session.')
-
-        logger.info('Computing signal decomposition on epochs. '
-                    'Please be patient, this may take some time')
-
-        if picks is None:  # just use epochs good data channels and avoid
-            picks = pick_types(epochs.info, include=epochs.ch_names,  # double
-                               exclude=epochs.info['bads'])  # picking
-
-        meeg_picks = pick_types(epochs.info, meg=True, eeg=True, eog=False,
-                                ecg=False, misc=False, stim=False,
-                                exclude=epochs.info['bads'])
-
-        # filter out all the channels the raw wouldn't have initialized
-        picks = np.intersect1d(meeg_picks, picks)
-
-        self.ch_names = [epochs.ch_names[k] for k in picks]
-
-        if self.max_n_components is None:
-            self.max_n_components = len(picks)
-            logger.info('Inferring max_n_components from picks.')
-
-        data, self._pre_whitener = self._pre_whiten(
-                                np.hstack(epochs.get_data()[:, picks]),
-                                epochs.info, picks)
-
-        to_ica, self._pca = self._prepare_pca(data, self.max_n_components)
-
-        self._ica.fit(to_ica)
-        self._mixing = self._ica.get_mixing_matrix().T
-        self.current_fit = 'epochs'
-
-        return self
-
-    @deprecated(_preprocessing)
-    def get_sources_raw(self, raw, start=None, stop=None):
-        """Estimate raw sources given the unmixing matrix
-
-        Parameters
-        ----------
-        raw : instance of Raw
-            Raw object to draw sources from.
-        start : int
-            First sample to include (first is 0). If omitted, defaults to the
-            first sample in data.
-        stop : int
-            First sample to not include.
-            If omitted, data is included to the end.
-
-        Returns
-        -------
-        sources : array, shape = (n_components, n_times)
-            The ICA sources time series.
-        """
-        if self._mixing is None:
-            raise RuntimeError('No fit available. Please first fit ICA '
-                               'decomposition.')
-
-        return self._get_sources_raw(raw, start, stop)[0]
-
-    def _get_sources_raw(self, raw, start, stop):
-        picks = [raw.ch_names.index(k) for k in self.ch_names]
-        data, _ = self._pre_whiten(raw[picks, start:stop][0], raw.info, picks)
-        pca_data = self._pca.transform(data.T)
-        raw_sources = self._ica.transform(pca_data[:, self._comp_idx]).T
-
-        return raw_sources, pca_data
-
-    @deprecated(_preprocessing)
-    def get_sources_epochs(self, epochs, concatenate=False):
-        """Estimate epochs sources given the unmixing matrix
-
-        Parameters
-        ----------
-        epochs : instance of Epochs
-            Epochs object to draw sources from.
-        concatenate : bool
-            If true, epochs and time slices will be concatenated.
-
-        Returns
-        -------
-        epochs_sources : ndarray of shape (n_epochs, n_sources, n_times)
-            The sources for each epoch
-        """
-        if self._mixing is None:
-            raise RuntimeError('No fit available. Please first fit ICA '
-                               'decomposition.')
-
-        return self._get_sources_epochs(epochs, concatenate)[0]
-
-    def _get_sources_epochs(self, epochs, concatenate):
-
-        picks = pick_types(epochs.info, include=self.ch_names,
-                               exclude=epochs.info['bads'])
-
-        # special case where epochs come picked but fit was 'unpicked'.
-        if len(picks) != len(self.ch_names):
-            raise RuntimeError('Epochs don\'t match fitted data: %i channels '
-                               'fitted but %i channels supplied. \nPlease '
-                               'provide Epochs compatible with '
-                               'ica.ch_names' % (len(self.ch_names),
-                                                  len(picks)))
-
-        data, _ = self._pre_whiten(np.hstack(epochs.get_data()[:, picks]),
-                                   epochs.info, picks)
-
-        pca_data = self._pca.transform(data.T)
-        sources = self._ica.transform(pca_data[:, self._comp_idx]).T
-        sources = np.array(np.split(sources, len(epochs.events), 1))
-
-        if concatenate:
-            sources = np.hstack(sources)
-
-        return sources, pca_data
-
-    @deprecated(_preprocessing)
-    def export_sources(self, raw, picks=None, start=None, stop=None):
-        """Export sources as raw object
-
-        Parameters
-        ----------
-        raw : instance of Raw
-            Raw object to export sources from.
-        picks : array-like
-            Channels to be included in addition to the sources. If None,
-            artifact and stimulus channels will be included.
-        start : int
-            First sample to include (first is 0). If omitted, defaults to the
-            first sample in data.
-        stop : int
-            First sample to not include. If omitted, data is included to the
-            end.
-
-        Returns
-        -------
-        out : instance of mne.Raw
-            Container object for ICA sources
-
-        """
-        if not raw._preloaded:
-            raise ValueError('raw data should be preloaded to have this '
-                             'working. Please read raw data with '
-                             'preload=True.')
-
-        # include 'reference' channels for comparison with ICA
-        if picks is None:
-            picks = pick_types(raw.info, meg=False, eeg=False, misc=True,
-                               ecg=True, eog=True, stim=True)
-
-        # merge copied instance and picked data with sources
-        out = raw.copy()
-        out.fids = []
-        sources = self.get_sources_raw(raw, start=start, stop=stop)
-        out._data = np.r_[sources, raw[picks, start:stop][0]]
-
-        # update first and last samples
-        out.first_samp = raw.first_samp + (start if start else 0)
-        out.last_samp = out.first_samp + stop if stop else raw.last_samp
-
-        # set channel names and info
-        ch_names = out.info['ch_names'] = []
-        ch_info = out.info['chs'] = []
-        for i in xrange(self.n_components):
-            ch_names.append('ICA %03d' % (i + 1))
-            ch_info.append(dict(ch_name='ICA %03d' % (i + 1), cal=1,
-                logno=i + 1, coil_type=FIFF.FIFFV_COIL_NONE,
-                kind=FIFF.FIFFV_MISC_CH, coord_Frame=FIFF.FIFFV_COORD_UNKNOWN,
-                loc=np.array([0.,  0.,  0.,  1., 0.,  0.,  0.,  1.,
-                              0.,  0.,  0.,  1.], dtype=np.float32),
-                unit=FIFF.FIFF_UNIT_NONE, eeg_loc=None, range=1.0,
-                scanno=i + 1, unit_mul=0, coil_trans=None))
-
-        # re-append additionally picked ch_names
-        ch_names += [raw.ch_names[k] for k in picks]
-        # re-append additionally picked ch_info
-        ch_info += [raw.info['chs'][k] for k in picks]
-
-        # update number of channels
-        out.info['nchan'] = len(picks) + self.n_components
-
-        return out
-
-    @deprecated(_preprocessing)
-    def plot_sources_raw(self, raw, order=None, start=None, stop=None,
-                         n_components=None, source_idx=None, ncol=3, nrow=10,
-                         show=True):
-        """Create panel plots of ICA sources. Wrapper around viz.plot_ica_panel
-
-        Parameters
-        ----------
-        raw : instance of mne.fiff.Raw
-            Raw object to plot the sources from.
-        order : ndarray | None.
-            Index of length n_components. If None, plot will show the sources
-            in the order as fitted.
-            Example: arg_sort = np.argsort(np.var(sources)).
-        start : int
-            X-axis start index. If None from the beginning.
-        stop : int
-            X-axis stop index. If None to the end.
-        n_components : int
-            Number of components fitted.
-        source_idx : array-like
-            Indices for subsetting the sources.
-        ncol : int
-            Number of panel-columns.
-        nrow : int
-            Number of panel-rows.
-        show : bool
-            If True, plot will be shown, else just the figure is returned.
-
-        Returns
-        -------
-        fig : instance of pyplot.Figure
-        """
-
-        sources = self.get_sources_raw(raw, start=start, stop=stop)
-
-        if order is not None:
-            if len(order) != sources.shape[0]:
-                    raise ValueError('order and sources have to be of the '
-                                     'same length.')
-            else:
-                sources = sources[order]
-
-        fig = plot_ica_panel(sources, start=0 if start is not None else start,
-                             stop=(stop - start) if stop is not None else stop,
-                             n_components=n_components, source_idx=source_idx,
-                             ncol=ncol, nrow=nrow)
-        if show:
-            import matplotlib.pylab as pl
-            pl.show()
-
-        return fig
-
-    @deprecated(_preprocessing)
-    def plot_sources_epochs(self, epochs, epoch_idx=None, order=None,
-                            start=None, stop=None, n_components=None,
-                            source_idx=None, ncol=3, nrow=10, show=True):
-        """Create panel plots of ICA sources. Wrapper around viz.plot_ica_panel
-
-        Parameters
-        ----------
-        epochs : instance of mne.Epochs
-            Epochs object to plot the sources from.
-        epoch_idx : int
-            Index to plot particular epoch.
-        order : ndarray | None.
-            Index of length n_components. If None, plot will show the sources
-            in the order as fitted.
-            Example: arg_sort = np.argsort(np.var(sources)).
-        sources : ndarray
-            Sources as drawn from self.get_sources.
-        start : int
-            X-axis start index. If None from the beginning.
-        stop : int
-            X-axis stop index. If None to the end.
-        n_components : int
-            Number of components fitted.
-        source_idx : array-like
-            Indices for subsetting the sources.
-        ncol : int
-            Number of panel-columns.
-        nrow : int
-            Number of panel-rows.
-        show : bool
-            If True, plot will be shown, else just the figure is returned.
-
-        Returns
-        -------
-        fig : instance of pyplot.Figure
-        """
-        sources = self.get_sources_epochs(epochs, concatenate=True if epoch_idx
-                                          is None else False)
-        source_dim = 1 if sources.ndim > 2 else 0
-        if order is not None:
-            if len(order) != sources.shape[source_dim]:
-                raise ValueError('order and sources have to be of the '
-                                 'same length.')
-            else:
-                sources = (sources[:, order] if source_dim
-                           else sources[order])
-
-        fig = plot_ica_panel(sources[epoch_idx], start=start, stop=stop,
-                             n_components=n_components, source_idx=source_idx,
-                             ncol=ncol, nrow=nrow)
-        if show:
-            import matplotlib.pylab as pl
-            pl.show()
-
-        return fig
-
-    @deprecated(_preprocessing)
-    def find_sources_raw(self, raw, target=None, score_func='pearsonr',
-                         start=None, stop=None):
-        """Find sources based on own distribution or based on similarity to
-        other sources or between source and target.
-
-        Parameters
-        ----------
-        raw : instance of Raw
-            Raw object to draw sources from.
-        target : array-like | ch_name | None
-            Signal to which the sources shall be compared. It has to be of
-            the same shape as the sources. If some string is supplied, a
-            routine will try to find a matching channel. If None, a score
-            function expecting only one input-array argument must be used,
-            for instance, scipy.stats.skew (default).
-        score_func : callable | str label
-            Callable taking as arguments either two input arrays
-            (e.g. pearson correlation) or one input
-            array (e. g. skewness) and returns a float. For convenience the
-            most common score_funcs are available via string labels: Currently,
-            all distance metrics from scipy.spatial and all functions from
-            scipy.stats taking compatible input arguments are supported. These
-            function have been modified to support iteration over the rows of a
-            2D array.
-        start : int
-            First sample to include (first is 0). If omitted, defaults to the
-            first sample in data.
-        stop : int
-            First sample to not include.
-            If omitted, data is included to the end.
-        scores : ndarray
-            Scores for each source as returned from score_func.
-
-        Returns
-        -------
-        scores : ndarray
-            scores for each source as returned from score_func
-        """
-        # auto source drawing
-        sources = self.get_sources_raw(raw=raw, start=start, stop=stop)
-
-        # auto target selection
-        if target is not None:
-            if hasattr(target, 'ndim'):
-                if target.ndim < 2:
-                    target = target.reshape(1, target.shape[-1])
-            if isinstance(target, str):
-                pick = _get_target_ch(raw, target)
-                target, _ = raw[pick, start:stop]
-            if sources.shape[1] != target.shape[1]:
-                raise ValueError('Source and targets do not have the same'
-                                 'number of time slices.')
-            target = target.ravel()
-
-        return _find_sources(sources, target, score_func)
-
-    @deprecated(_preprocessing)
-    def find_sources_epochs(self, epochs, target=None, score_func='pearsonr'):
-        """Find sources based on relations between source and target
-
-        Parameters
-        ----------
-        epochs : instance of Epochs
-            Epochs object to draw sources from.
-        target : array-like | ch_name | None
-            Signal to which the sources shall be compared. It has to be of
-            the same shape as the sources. If some string is supplied, a
-            routine will try to find a matching channel. If None, a score
-            function expecting only one input-array argument must be used,
-            for instance, scipy.stats.skew (default).
-        score_func : callable | str label
-            Callable taking as arguments either two input arrays
-            (e.g. pearson correlation) or one input
-            array (e. g. skewness) and returns a float. For convenience the
-            most common score_funcs are available via string labels: Currently,
-            all distance metrics from scipy.spatial and all functions from
-            scipy.stats taking compatible input arguments are supported. These
-            function have been modified to support iteration over the rows of a
-            2D array.
-
-        Returns
-        -------
-        scores : ndarray
-            scores for each source as returned from score_func
-        """
-        sources = self.get_sources_epochs(epochs=epochs)
-        # auto target selection
-        if target is not None:
-            if hasattr(target, 'ndim'):
-                if target.ndim < 3:
-                    target = target.reshape(1, 1, target.shape[-1])
-            if isinstance(target, str):
-                pick = _get_target_ch(epochs, target)
-                target = epochs.get_data()[:, pick]
-            if sources.shape[2] != target.shape[2]:
-                raise ValueError('Source and targets do not have the same'
-                                 'number of time slices.')
-            target = target.ravel()
-
-        return _find_sources(np.hstack(sources), target, score_func)
-
-    @deprecated(_preprocessing)
-    def pick_sources_raw(self, raw, include=None, exclude=None,
-                         n_pca_components=64, start=None, stop=None,
-                         copy=True):
-        """Recompose raw data including or excluding some sources
-
-        Parameters
-        ----------
-        raw : instance of Raw
-            Raw object to pick to remove ICA components from.
-        include : list-like | None
-            The source indices to use. If None all are used.
-        exclude : list-like | None
-            The source indices to remove. If None  all are used.
-        n_pca_components:
-            The number of PCA components to be unwhitened, where n_components
-            is the lower bound and max_n_components the upper bound.
-            If greater than self.n_components, the PCA components that were not
-            supplied to the ICA will get re-attached. This can be used to take
-            back the PCA dimension reduction.
-        start : int | None
-            The first time index to include.
-        stop : int | None
-            The first time index to exclude.
-        copy: bool
-            modify raw instance in place or return modified copy.
-
-        Returns
-        -------
-        raw : instance of Raw
-            raw instance with selected ICA components removed
-        """
-        if not raw._preloaded:
-            raise ValueError('raw data should be preloaded to have this '
-                             'working. Please read raw data with '
-                             'preload=True.')
-
-        if self.current_fit != 'raw':
-            raise ValueError('Currently no raw data fitted.'
-                             'Please fit raw data first.')
-
-        sources, pca_data = self._get_sources_raw(raw, start=start, stop=stop)
-        recomposed = self._pick_sources(sources, pca_data, include, exclude,
-                                        n_pca_components)
-
-        if copy is True:
-            raw = raw.copy()
-
-        picks = [raw.ch_names.index(k) for k in self.ch_names]
-        raw[picks, start:stop] = recomposed
-        return raw
-
-    @deprecated(_preprocessing)
-    def pick_sources_epochs(self, epochs, include=None, exclude=None,
-                            n_pca_components=64, copy=True):
-        """Recompose epochs
-
-        Parameters
-        ----------
-        epochs : instance of Epochs
-            Epochs object to pick to remove ICA components from.
-        include : list-like | None
-            The source indices to use. If None all are used.
-        exclude : list-like | None
-            The source indices to remove. If None  all are used.
-        n_pca_components:
-            The number of PCA components to be unwhitened, where n_components
-            is the lower bound and max_n_components the upper bound.
-            If greater than self.n_components, the PCA components that were not
-            supplied to the ICA will get re-attached. This can be used to take
-            back the PCA dimension reduction.
-        copy : bool
-            Modify Epochs instance in place or return modified copy.
-
-        Returns
-        -------
-        epochs : instance of Epochs
-            Epochs with selected ICA components removed.
-        """
-        if not epochs.preload:
-            raise ValueError('raw data should be preloaded to have this '
-                             'working. Please read raw data with '
-                             'preload=True.')
-
-        sources, pca_data = self._get_sources_epochs(epochs, True)
-        picks = pick_types(epochs.info, include=self.ch_names,
-                               exclude=epochs.info['bads'])
-
-        if copy is True:
-            epochs = epochs.copy()
-
-        # put sources-dimension first for selection
-        recomposed = self._pick_sources(sources, pca_data, include, exclude,
-                                        n_pca_components)
-        # restore epochs, channels, tsl order
-        epochs._data[:, picks] = np.array(np.split(recomposed,
-                                          len(epochs.events), 1))
-        epochs.preload = True
-
-        return epochs
-
-    def _pre_whiten(self, data, info, picks):
-        """Helper function"""
-        if self.noise_cov is None:  # use standardization as whitener
-            pre_whitener = np.std(data) ** -1
-            data *= pre_whitener
-        else:  # pick cov
-            ncov = deepcopy(self.noise_cov)
-            if ncov.ch_names != self.ch_names:
-                ncov['data'] = ncov.data[picks][:, picks]
-            assert data.shape[0] == ncov.data.shape[0]
-            pre_whitener, _ = compute_whitener(ncov, info, picks)
-            data = np.dot(pre_whitener, data)
-
-        return data, pre_whitener
-
-    def _prepare_pca(self, data, max_n_components):
-        """ Helper Function """
-        from sklearn.decomposition import RandomizedPCA
-
-        # sklearn < 0.11 does not support random_state argument
-        kwargs = {'n_components': max_n_components, 'whiten': False}
-
-        aspec = inspect.getargspec(RandomizedPCA.__init__)
-        if 'random_state' not in aspec.args:
-            warnings.warn('RandomizedPCA does not support random_state '
-                          'argument. Use scikit-learn to version 0.11 '
-                          'or newer to get reproducible results.')
-        else:
-            kwargs['random_state'] = 0
-
-        pca = RandomizedPCA(**kwargs)
-        pca_data = pca.fit_transform(data.T)
-
-        if self._explained_var > 1.0:
-            if self.n_components is not None:  # normal n case
-                self._comp_idx = np.arange(self.n_components)
-                to_ica = pca_data[:, self._comp_idx]
-            else:  # None case
-                to_ica = pca_data
-                self.n_components = pca_data.shape[1]
-                self._comp_idx = np.arange(self.n_components)
-        else:  # float case
-            expl_var = pca.explained_variance_ratio_
-            self._comp_idx = (np.where(expl_var.cumsum() <
-                                      self._explained_var)[0])
-            to_ica = pca_data[:, self._comp_idx]
-            self.n_components = len(self._comp_idx)
-
-        return to_ica, pca
-
-    def _pick_sources(self, sources, pca_data, include, exclude,
-                      n_pca_components):
-        """Helper function"""
-        if not(self.n_components <= n_pca_components <= self.max_n_components):
-            raise ValueError('n_pca_components must be between n_components'
-                             ' and max_n_components.')
-
-        if include not in (None, []):
-            mute = [i for i in xrange(len(sources)) if i not in include]
-            sources[mute, :] = 0.  # include via exclusion
-        elif exclude not in (None, []):
-            sources[exclude, :] = 0.  # just exclude
-
-        # restore pca data
-        mixing = self._mixing.copy()
-        pca_restored = np.dot(sources.T, mixing)
-
-        # re-append deselected pca dimension if desired
-        if n_pca_components - self.n_components > 0:
-            add_components = np.arange(self.n_components, n_pca_components)
-            pca_reappend = pca_data[:, add_components]
-            pca_restored = np.c_[pca_restored, pca_reappend]
-
-        # restore sensor space data
-        out = _inverse_t_pca(pca_restored, self._pca)
-
-        # restore scaling
-        pre_whitener = self._pre_whitener.copy()
-        if self.noise_cov is None:  # revert standardization
-            pre_whitener **= -1
-            out *= pre_whitener
-        else:
-            out = np.dot(out, linalg.pinv(pre_whitener))
-
-        return out.T
-
-
-@deprecated(_preprocessing)
-@verbose
-def ica_find_ecg_events(raw, ecg_source, event_id=999,
-                        tstart=0.0, l_freq=5, h_freq=35, qrs_threshold=0.6,
-                        verbose=None):
-    """Find ECG peaks from one selected ICA source
-
-    Parameters
-    ----------
-    ecg_source : ndarray
-        ICA source resembling ECG to find peaks from.
-    event_id : int
-        The index to assign to found events.
-    raw : instance of Raw
-        Raw object to draw sources from.
-    tstart : float
-        Start detection after tstart seconds. Useful when beginning
-        of run is noisy.
-    l_freq : float
-        Low pass frequency.
-    h_freq : float
-        High pass frequency.
-    qrs_threshold : float
-        Between 0 and 1. qrs detection threshold.
-    verbose : bool, str, int, or None
-        If not None, override default verbose level (see mne.verbose).
-
-    Returns
-    -------
-    ecg_events : array
-        Events.
-    ch_ECG : string
-        Name of channel used.
-    average_pulse : float.
-        Estimated average pulse.
-    """
-    logger.info('Using ICA source to identify heart beats')
-
-    # detecting QRS and generating event file
-    ecg_events = qrs_detector(raw.info['sfreq'], ecg_source.ravel(),
-                              tstart=tstart, thresh_value=qrs_threshold,
-                              l_freq=l_freq, h_freq=h_freq)
-
-    n_events = len(ecg_events)
-
-    ecg_events = np.c_[ecg_events + raw.first_samp, np.zeros(n_events),
-                       event_id * np.ones(n_events)]
-
-    return ecg_events
-
-
-@deprecated(_preprocessing)
-@verbose
-def ica_find_eog_events(raw, eog_source=None, event_id=998, l_freq=1,
-                        h_freq=10, verbose=None):
-    """Locate EOG artifacts
-
-    Parameters
-    ----------
-    raw : instance of Raw
-        The raw data.
-    eog_source : ndarray
-        ICA source resembling EOG to find peaks from.
-    event_id : int
-        The index to assign to found events.
-    low_pass : float
-        Low pass frequency.
-    high_pass : float
-        High pass frequency.
-    verbose : bool, str, int, or None
-        If not None, override default verbose level (see mne.verbose).
-
-    Returns
-    -------
-    eog_events : array
-        Events
-    """
-    eog_events = _find_eog_events(eog_source[np.newaxis], event_id=event_id,
-                                l_freq=l_freq, h_freq=h_freq,
-                                sampling_rate=raw.info['sfreq'],
-                                first_samp=raw.first_samp)
-    return eog_events
-
-
-def _get_target_ch(container, target):
-    """Helper Function"""
-    # auto target selection
-    pick = pick_channels(container.ch_names, include=[target])
-    if len(pick) == 0:
-        raise ValueError('%s not in channel list (%s)' %
-                        (target, container.ch_names))
-    return pick
-
-
-def _find_sources(sources, target, score_func):
-    """Helper Function"""
-    if isinstance(score_func, str):
-        score_func = score_funcs.get(score_func, score_func)
-
-    if not callable(score_func):
-        raise ValueError('%s is not a valid score_func.' % score_func)
-
-    scores = (score_func(sources, target) if target is not None
-              else score_func(sources, 1))
-
-    return scores
-
-
-def _inverse_t_pca(X, pca):
-    """Helper Function"""
-    components = pca.components_[np.arange(len(X.T))]
-    X_orig = np.dot(X, components)
-
-    if pca.mean_ is not None:
-        X_orig += pca.mean_
-
-    return X_orig
diff --git a/mne/artifacts/stim.py b/mne/artifacts/stim.py
deleted file mode 100644
index faefb1adca6..00000000000
--- a/mne/artifacts/stim.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Authors: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
-#
-# License: BSD (3-clause)
-
-import numpy as np
-from scipy import signal, interpolate
-
-from ..fiff import pick_types
-from .. utils import deprecated
-
-
-_preprocessing = ('The artifacts module will no longer be supported.'
-                  ' Please use the preprocessing module instead.')
-
-
-@deprecated(_preprocessing)
-def eliminate_stim_artifact(raw, events, event_id, tmin=-0.005,
-                            tmax=0.01, mode='linear'):
-    """Eliminates stimulations artifacts from raw data
-
-    The raw object will be modified in place (no copy)
-
-    Parameters
-    ----------
-    raw: Raw object
-        raw data object
-    events: array, shape (n_events, 3)
-        The list of events
-    event_id: int
-        The id of the events generating the stimulation artifacts.
-    tmin : float
-        Start time before event in seconds
-    tmax : float
-        End time after event in seconds
-    mode : 'linear' | 'window'
-        way to fill the artifacted time interval
-        'linear' does linear interpolation
-        'window' applies a (1 - hanning) window
-
-    Returns
-    -------
-    raw: Raw object
-        raw data object
-    """
-    if not raw._preloaded:
-        raise RuntimeError('Modifying data of Raw is only supported '
-                            'when preloading is used. Use preload=True '
-                            '(or string) in the constructor.')
-    events_sel = (events[:, 2] == event_id)
-    event_start = events[events_sel, 0]
-    s_start = np.ceil(raw.info['sfreq'] * np.abs(tmin))
-    s_end = np.ceil(raw.info['sfreq'] * tmax)
-
-    picks = pick_types(raw.info, meg=True, eeg=True)
-
-    if mode == 'window':
-        window = 1 - np.r_[signal.hann(4)[:2], np.ones(s_end + s_start - 4),
-                            signal.hann(4)[-2:]].T
-
-    for k in range(len(event_start)):
-        first_samp = event_start[k] - raw.first_samp - s_start
-        last_samp = event_start[k] - raw.first_samp + s_end
-        data, _ = raw[picks, first_samp:last_samp]
-        if mode == 'linear':
-            x = np.array([first_samp, last_samp])
-            f = interpolate.interp1d(x, data[:, (0, -1)])
-            xnew = np.arange(first_samp, last_samp)
-            interp_data = f(xnew)
-            raw[picks, first_samp:last_samp] = interp_data
-        elif mode == 'window':
-            raw[picks, first_samp:last_samp] = data * window[np.newaxis, :]
-    return raw
diff --git a/mne/artifacts/tests/__init__.py b/mne/artifacts/tests/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/mne/artifacts/tests/test_ecg.py b/mne/artifacts/tests/test_ecg.py
deleted file mode 100644
index 4c994e0f27c..00000000000
--- a/mne/artifacts/tests/test_ecg.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os.path as op
-
-from nose.tools import assert_true
-
-from mne.fiff import Raw
-from mne.artifacts.ecg import find_ecg_events
-
-data_path = op.join(op.dirname(__file__), '..', '..', 'fiff', 'tests', 'data')
-raw_fname = op.join(data_path, 'test_raw.fif')
-event_fname = op.join(data_path, 'test-eve.fif')
-proj_fname = op.join(data_path, 'test_proj.fif')
-
-
-def test_find_ecg():
-    """Test find ECG peaks"""
-    raw = Raw(raw_fname)
-    events, ch_ECG, average_pulse = find_ecg_events(raw, event_id=999,
-                                                    ch_name='MEG 1531')
-    n_events = len(events)
-    _, times = raw[0, :]
-    assert_true(55 < average_pulse < 60)
diff --git a/mne/artifacts/tests/test_eog.py b/mne/artifacts/tests/test_eog.py
deleted file mode 100644
index 087c6927786..00000000000
--- a/mne/artifacts/tests/test_eog.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os.path as op
-from nose.tools import assert_true
-
-from mne.fiff import Raw
-from mne.artifacts.eog import find_eog_events
-
-data_path = op.join(op.dirname(__file__), '..', '..', 'fiff', 'tests', 'data')
-raw_fname = op.join(data_path, 'test_raw.fif')
-event_fname = op.join(data_path, 'test-eve.fif')
-proj_fname = op.join(data_path, 'test_proj.fif')
-
-
-def test_find_eog():
-    """Test find EOG peaks"""
-    raw = Raw(raw_fname)
-    events = find_eog_events(raw)
-    n_events = len(events)
-    assert_true(n_events == 4)
diff --git a/mne/artifacts/tests/test_peak_finder.py b/mne/artifacts/tests/test_peak_finder.py
deleted file mode 100644
index 5d607199f56..00000000000
--- a/mne/artifacts/tests/test_peak_finder.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from numpy.testing import assert_array_equal
-
-from mne.artifacts.peak_finder import peak_finder
-
-
-def test_peak_finder():
-    """Test the peak detection method"""
-    x = [0, 2, 5, 0, 6, -1]
-    peak_inds, peak_mags = peak_finder(x)
-    assert_array_equal(peak_inds, [2, 4])
diff --git a/mne/artifacts/tests/test_stim.py b/mne/artifacts/tests/test_stim.py
deleted file mode 100644
index 53bb1ea712e..00000000000
--- a/mne/artifacts/tests/test_stim.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Authors: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
-#
-# License: BSD (3-clause)
-
-import os.path as op
-
-import numpy as np
-from numpy.testing import assert_array_almost_equal
-from nose.tools import assert_true
-
-from mne.fiff import Raw
-from mne.event import read_events
-from mne.artifacts.stim import eliminate_stim_artifact
-
-data_path = op.join(op.dirname(__file__), '..', '..', 'fiff', 'tests', 'data')
-raw_fname = op.join(data_path, 'test_raw.fif')
-event_fname = op.join(data_path, 'test-eve.fif')
-
-
-def test_stim_elim():
-    """Test eliminate stim artifact"""
-    raw = Raw(raw_fname, preload=True)
-    events = read_events(event_fname)
-    event_idx = np.where(events[:, 2] == 1)[0][0]
-    tidx = events[event_idx, 0] - raw.first_samp
-
-    raw = eliminate_stim_artifact(raw, events, event_id=1, tmin=-0.005,
-                                  tmax=0.01, mode='linear')
-    data, times = raw[:, tidx - 3:tidx + 5]
-    diff_data0 = np.diff(data[0])
-    diff_data0 -= np.mean(diff_data0)
-    assert_array_almost_equal(diff_data0, np.zeros(len(diff_data0)))
-    raw = eliminate_stim_artifact(raw, events, event_id=1, tmin=-0.005,
-                                  tmax=0.01, mode='window')
-    data, times = raw[:, tidx:tidx + 1]
-    assert_true(np.all(data) == 0.)
diff --git a/mne/preprocessing/__init__.py b/mne/preprocessing/__init__.py
index 18fcdc7e199..f2699f87593 100644
--- a/mne/preprocessing/__init__.py
+++ b/mne/preprocessing/__init__.py
@@ -7,8 +7,8 @@
 #
 # License: BSD (3-clause)
 
-from . maxfilter import apply_maxfilter
-from . ssp import compute_proj_ecg, compute_proj_eog
-from . eog import find_eog_events
-from . ecg import find_ecg_events
-from . ica import ICA, ica_find_eog_events, ica_find_ecg_events, score_funcs
\ No newline at end of file
+from .maxfilter import apply_maxfilter
+from .ssp import compute_proj_ecg, compute_proj_eog
+from .eog import find_eog_events
+from .ecg import find_ecg_events
+from .ica import ICA, ica_find_eog_events, ica_find_ecg_events, score_funcs
\ No newline at end of file
diff --git a/mne/artifacts/peak_finder.py b/mne/preprocessing/peak_finder.py
similarity index 97%
rename from mne/artifacts/peak_finder.py
rename to mne/preprocessing/peak_finder.py
index eaf763712b3..b2630569292 100644
--- a/mne/artifacts/peak_finder.py
+++ b/mne/preprocessing/peak_finder.py
@@ -8,11 +8,6 @@
 from .. utils import deprecated
 
 
-_preprocessing = ('The artifacts module will no longer be supported.'
-                  ' Please use the preprocessing module instead.')
-
-
-@deprecated(_preprocessing)
 @verbose
 def peak_finder(x0, thresh=None, extrema=1, verbose=None):
     """Noise tolerant fast peak finding algorithm
diff --git a/mne/preprocessing/ssp.py b/mne/preprocessing/ssp.py
index c24f50d4080..abf89f74283 100644
--- a/mne/preprocessing/ssp.py
+++ b/mne/preprocessing/ssp.py
@@ -4,16 +4,17 @@
 #
 # License: BSD (3-clause)
 
-import numpy as np
 import copy as cp
+from warnings import warn
+import numpy as np
 
 import logging
 logger = logging.getLogger('mne')
 
 from .. import Epochs, compute_proj_evoked, compute_proj_epochs, verbose
 from ..fiff import pick_types, make_eeg_average_ref_proj
-from ..artifacts import find_ecg_events, find_eog_events
-from warnings import warn
+from .ecg import find_ecg_events
+from .eog import find_eog_events
 
 
 @verbose
diff --git a/mne/artifacts/tests/test_ica.py b/mne/preprocessing/tests/test_ica.py
similarity index 98%
rename from mne/artifacts/tests/test_ica.py
rename to mne/preprocessing/tests/test_ica.py
index b0df10a5f0a..0b211fe98f8 100644
--- a/mne/artifacts/tests/test_ica.py
+++ b/mne/preprocessing/tests/test_ica.py
@@ -11,8 +11,8 @@
 from itertools import product
 
 from mne import fiff, Epochs, read_events, cov
-from mne.artifacts import ICA, ica_find_ecg_events, ica_find_eog_events
-from mne.artifacts.ica import score_funcs
+from mne.preprocessing import ICA, ica_find_ecg_events, ica_find_eog_events
+from mne.preprocessing.ica import score_funcs
 
 have_sklearn = True
 try:
diff --git a/mne/utils.py b/mne/utils.py
index 4a6a3724c3a..a3c5f9a116f 100644
--- a/mne/utils.py
+++ b/mne/utils.py
@@ -17,6 +17,8 @@
 
 # Following deprecated class copied from scikit-learn
 
+# force show of DeprecationWarning even on python 2.7
+warnings.simplefilter('default')
 
 class deprecated(object):
     """Decorator to mark a function or class as deprecated.
diff --git a/setup.py b/setup.py
index 0c0f9de471d..6f3276bb9e5 100755
--- a/setup.py
+++ b/setup.py
@@ -51,7 +51,6 @@
              ],
          platforms='any',
          packages=['mne', 'mne.tests',
-                   'mne.artifacts', 'mne.artifacts.tests',
                    'mne.beamformer', 'mne.beamformer.tests',
                    'mne.connectivity', 'mne.connectivity.tests',
                    'mne.data',