diff --git a/externals/mne_openbci.py b/externals/mne_openbci.py
new file mode 100644
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+++ b/externals/mne_openbci.py
@@ -0,0 +1,232 @@
+"""Conversion tool from OpenBCI to MNE Raw Class"""
+
+# Authors: Teon Brooks <teon.brooks@gmail.com>
+#
+# License: BSD (3-clause)
+
+import warnings
+np = None
+try:
+    import numpy as np
+except ImportError:
+    raise ImportError('Numpy is needed to use function.')
+mne = None
+try:
+    from mne.utils import verbose, logger
+    from mne.io.meas_info import create_info
+    from mne.io.base import _BaseRaw
+except ImportError:
+    raise ImportError('MNE is needed to use function.')
+
+class RawOpenBCI(_BaseRaw):
+    """Raw object from OpenBCI file
+
+    Parameters
+    ----------
+    input_fname : str
+        Path to the OpenBCI file.
+    montage : str | None | instance of Montage
+        Path or instance of montage containing electrode positions.
+        If None, sensor locations are (0,0,0). See the documentation of
+        :func:`mne.channels.read_montage` for more information.
+    eog : list or tuple
+        Names of channels or list of indices that should be designated
+        EOG channels. Default is None.
+    misc : list or tuple
+        List of indices that should be designated MISC channels.
+        Default is (-3, -2, -1), which are the accelerator sensors.
+    stim_channel : int | None
+        The channel index (starting at 0).
+        If None (default), there will be no stim channel added.
+    scale : float
+        The scaling factor for EEG data. Units for MNE are in volts.
+        OpenBCI data are typically stored in microvolts. Default scale
+        factor is 1e-6.
+    sfreq : int
+        The sampling frequency of the data. OpenBCI defaults are 250 Hz.
+    missing_tol : int
+        The tolerance for interpolating missing samples. Default is 1. If the
+        number of contiguous missing samples is greater than tolerance, then
+        values are marked as NaN.
+    preload : bool
+        If True, all data are loaded at initialization.
+        If False, data are not read until save.
+    verbose : bool, str, int, or None
+        If not None, override default verbose level (see mne.verbose).
+
+
+    See Also
+    --------
+    mne.io.Raw : Documentation of attribute and methods.
+    """
+    @verbose
+    def __init__(self, input_fname, montage=None, eog=None,
+                 misc=(-3, -2, -1), stim_channel=None, scale=1e-6, sfreq=250,
+                 missing_tol=1, preload=True, verbose=None):
+
+        bci_info = {'missing_tol': missing_tol, 'stim_channel': stim_channel}
+        if not eog:
+            eog = list()
+        if not misc:
+            misc = list()
+        nsamps, nchan = self._get_data_dims(input_fname)
+
+        last_samps = [nsamps - 1]
+        ch_names = ['EEG %03d' % num for num in range(1, nchan + 1)]
+        ch_types = ['eeg'] * nchan
+        if misc:
+            misc_names = ['MISC %03d' % ii for ii in range(1, len(misc) + 1)]
+            misc_types = ['misc'] * len(misc)
+            for ii, mi in enumerate(misc):
+                ch_names[mi] = misc_names[ii]
+                ch_types[mi] = misc_types[ii]
+        if eog:
+            eog_names = ['EOG %03d' % ii for ii in range(len(eog))]
+            eog_types = ['eog'] * len(eog)
+            for ii, ei in enumerate(eog):
+                ch_names[ei] = eog_names[ii]
+                ch_types[ei] = eog_types[ii]
+        if stim_channel:
+            ch_names[stim_channel] = 'STI 014'
+            ch_types[stim_channel] = 'stim'
+
+        # fix it for eog and misc marking
+        info = create_info(ch_names, sfreq, ch_types, montage)
+        super(RawOpenBCI, self).__init__(info, last_samps=last_samps,
+                                         raw_extras=[bci_info],
+                                         filenames=[input_fname],
+                                         preload=False, verbose=verbose)
+        # load data
+        if preload:
+            self.preload = preload
+            logger.info('Reading raw data from %s...' % input_fname)
+            self._data, _ = self._read_segment()
+
+    def _read_segment_file(self, data, idx, offset, fi, start, stop,
+                           cals, mult):
+        """Read a chunk of raw data"""
+        input_fname = self._filenames[fi]
+        data_ = np.genfromtxt(input_fname, delimiter=',', comments='%',
+                              skip_footer=1)
+        """
+        Dealing with the missing data
+        -----------------------------
+        When recording with OpenBCI over Bluetooth, it is possible for some of
+        the data packets, samples, to not be recorded. This does not happen
+        often but it poses a problem for maintaining proper sampling periods.
+        OpenBCI data format combats this by providing a counter on the sample
+        to know which ones are missing.
+
+        Solution
+        --------
+        Interpolate the missing samples by resampling the surrounding samples.
+        1. Find where the missing samples are.
+        2. Deal with the counter reset (resets after cycling a byte).
+        3. Resample given the diffs.
+        4. Insert resampled data in the array using the diff indices
+           (index + 1).
+        5. If number of missing samples is greater than the missing_tol, Values
+           are replaced with np.nan.
+        """
+        # counter goes from 0 to 255, maxdiff is 255.
+        # make diff one like others.
+        missing_tol = self._raw_extras[fi]['missing_tol']
+        diff = np.abs(np.diff(data_[:, 0]))
+        diff = np.mod(diff, 254) - 1
+        missing_idx = np.where(diff != 0)[0]
+        missing_samps = diff[missing_idx].astype(int)
+
+        if missing_samps.size:
+            missing_nsamps = np.sum(missing_samps, dtype=int)
+            missing_cumsum = np.insert(np.cumsum(missing_samps), 0, 0)[:-1]
+            missing_data = np.empty((missing_nsamps, data_.shape[-1]),
+                                    dtype=float)
+            insert_idx = list()
+            for idx_, nn, ii in zip(missing_idx, missing_samps,
+                                    missing_cumsum):
+                missing_data[ii:ii + nn] = np.mean(data_[(idx_, idx_ + 1), :])
+                if nn > missing_tol:
+                    missing_data[ii:ii + nn] *= np.nan
+                    warnings.warn('The number of missing samples exceeded the '
+                                  'missing_tol threshold.')
+                insert_idx.append([idx_] * nn)
+            insert_idx = np.hstack(insert_idx)
+            data_ = np.insert(data_, insert_idx, missing_data, axis=0)
+        # data_ dimensions are samples by channels. transpose for MNE.
+        data_ = data_[start:stop, 1:].T
+        data[:, offset:offset + stop - start] = \
+            np.dot(mult, data_[idx]) if mult is not None else data_[idx]
+
+    def _get_data_dims(self, input_fname):
+        """Briefly scan the data file for info"""
+        # raw data formatting is nsamps by nchans + counter
+        data = np.genfromtxt(input_fname, delimiter=',', comments='%',
+                             skip_footer=1)
+        diff = np.abs(np.diff(data[:, 0]))
+        diff = np.mod(diff, 254) - 1
+        missing_idx = np.where(diff != 0)[0]
+        missing_samps = diff[missing_idx].astype(int)
+        nsamps, nchan = data.shape
+        # add the missing samples
+        nsamps += sum(missing_samps)
+        # remove the tracker column
+        nchan -= 1
+        del data
+
+        return nsamps, nchan
+
+
+def read_raw_openbci(input_fname, montage=None, eog=None, misc=(-3, -2, -1),
+                     stim_channel=None, scale=1e-6, sfreq=250, missing_tol=1,
+                     preload=True, verbose=None):
+    """Raw object from OpenBCI file
+
+    Parameters
+    ----------
+    input_fname : str
+        Path to the OpenBCI file.
+    montage : str | None | instance of Montage
+        Path or instance of montage containing electrode positions.
+        If None, sensor locations are (0,0,0). See the documentation of
+        :func:`mne.channels.read_montage` for more information.
+    eog : list or tuple
+        Names of channels or list of indices that should be designated
+        EOG channels. Default is None.
+    misc : list or tuple
+        List of indices that should be designated MISC channels.
+        Default is (-3, -2, -1), which are the accelerator sensors.
+    stim_channel : str | int | None
+        The channel name or channel index (starting at 0).
+        -1 corresponds to the last channel (default).
+        If None, there will be no stim channel added.
+    scale : float
+        The scaling factor for EEG data. Units for MNE are in volts.
+        OpenBCI data are typically stored in microvolts. Default scale
+        factor is 1e-6.
+    sfreq : int
+        The sampling frequency of the data. OpenBCI defaults are 250 Hz.
+    missing_tol : int
+        The tolerance for interpolating missing samples. Default is 1. If the
+        number of contiguous missing samples is greater than tolerance, then
+        values are marked as NaN.
+    preload : bool
+        If True, all data are loaded at initialization.
+        If False, data are not read until save.
+    verbose : bool, str, int, or None
+        If not None, override default verbose level (see mne.verbose).
+
+    Returns
+    -------
+    raw : Instance of RawOpenBCI
+        A Raw object containing OpenBCI data.
+
+
+    See Also
+    --------
+    mne.io.Raw : Documentation of attribute and methods.
+    """
+    raw = RawOpenBCI(input_fname=input_fname, montage=montage, eog=eog,
+                     misc=misc, stim_channel=stim_channel, scale=scale,
+                     sfreq=sfreq, missing_tol=missing_tol, preload=preload,
+                     verbose=verbose)
+    return raw