BrainFlow to MNE Python Notebook

import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import brainflow
from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds

import mne
from mne.channels import read_layout

# use synthetic board for demo
params = BrainFlowInputParams()
board = BoardShim(BoardIds.SYNTHETIC_BOARD.value, params)
board.prepare_session()
board.start_stream()
time.sleep(10)
data = board.get_board_data()
board.stop_stream()
board.release_session()

[2025-01-15 23:33:58.261] [board_logger] [info] incoming json: {
    "file": "",
    "file_anc": "",
    "file_aux": "",
    "ip_address": "",
    "ip_address_anc": "",
    "ip_address_aux": "",
    "ip_port": 0,
    "ip_port_anc": 0,
    "ip_port_aux": 0,
    "ip_protocol": 0,
    "mac_address": "",
    "master_board": -100,
    "other_info": "",
    "serial_number": "",
    "serial_port": "",
    "timeout": 0
}

eeg_channels = BoardShim.get_eeg_channels(BoardIds.SYNTHETIC_BOARD.value)
eeg_data = data[eeg_channels, :]
eeg_data = eeg_data / 1000000 # BrainFlow returns uV, convert to V for MNE

# Creating MNE objects from brainflow data arrays
ch_types = ['eeg'] * len(eeg_channels)
ch_names = BoardShim.get_eeg_names(BoardIds.SYNTHETIC_BOARD.value)
sfreq = BoardShim.get_sampling_rate(BoardIds.SYNTHETIC_BOARD.value)
info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
raw = mne.io.RawArray(eeg_data, info)
# its time to plot something!
raw.plot_psd(average=False)

Creating RawArray with float64 data, n_channels=16, n_times=2502
    Range : 0 ... 2501 =      0.000 ...    10.004 secs
Ready.
NOTE: plot_psd() is a legacy function. New code should use .compute_psd().plot().
Effective window size : 8.192 (s)

/tmp/ipykernel_840/1429959458.py:8: RuntimeWarning: Channel locations not available. Disabling spatial colors.
  raw.plot_psd(average=False)