MNE Python

###########################
### Mentalab MNE Loader ###
###########################
# The below script can be used to create an mne raw data object
# from a csv or bdf file recorded with Explore Desktop,
# or an xdf file recorded with LabRecorder.
#
# Loading xdf files selects the first stream in the file by default.
# Change it by adjusting the stream_num variable.
# Make sure to select the correct ExG stream, as there may be several ExG,
# ORN, and Marker streams in one xdf file.
#
# To load a Mentalab binary (*.BIN) taken from the amplifiers flash memory,
# convert it to csv or bdf using Explore Dekstop (File > Convert BIN) first
# and then continue with the below function.
import pandas as pd
import pyxdf
import mne

def mne_load_mentalab(file_name):
    # CSV
    if file_name.split('.')[1] == "csv":
        print(f"Creating mne raw object from csv.")

        file_name_root = file_name.split('.')[0][:-4]
        sampling_freq = pd.read_csv(file_name_root + '_Meta.csv', delimiter=',')['sr'][0]

        data_frame = pd.read_csv(file_name, delimiter=',')
        data_frame = data_frame.drop('TimeStamp', axis = 1)
        n_channels = len(data_frame.columns)
        ch_types = ["eeg"] * n_channels
        ch_names = list(data_frame.columns)

        info = mne.create_info(ch_names=ch_names,
                            sfreq=sampling_freq,
                            ch_types=ch_types)
        # convert from muVolt to Volt because MNE expects data in Volt units
        data_frame = data_frame.div(1e6)
        data_frame = data_frame.transpose()
        raw_data = mne.io.RawArray(data_frame, info)

        return raw_data

    # BDF
    elif file_name.split('.')[1] == 'bdf':
        print(f"Creating mne raw object from bdf.")

        raw_data = mne.io.read_raw_bdf(file_name)

        return raw_data
    # XDF
    elif file_name.split('.')[1] == 'xdf':
        streams, header = pyxdf.load_xdf(file_name)

        # THIS SELECTS THE FIRST STREAM
        # CHANGE INDEX TO SWITCH STREAM
        stream_num = 1
        print(f"Creating mne raw object from xdf. Selected stream: {stream_num}.")
        ch_names = []
        for x in streams[stream_num]['info']['desc'][0]['channels'][0]['channel']:
            ch_names.append(x['name'][0])
        data = streams[stream_num]["time_series"].T

        n_channels = data.shape[0]
        sampling_freq = float(streams[stream_num]["info"]["nominal_srate"][0])
        # convert from muVolt to Volt because MNE expects data in Volt units
        data *= 1e-6  # uV -> V
        info = mne.create_info(ch_names, sampling_freq, ["eeg"] * n_channels)
        raw_data = mne.io.RawArray(data, info)

        return raw_data

# Usage example calls
raw_data = mne_load_mentalab("data/8channel_ExG.csv")
raw_data = mne_load_mentalab("data/16channel_ExG.bdf")
raw_data = mne_load_mentalab("data/32channel_ExG.xdf")