BrainFlow Band Power Notebook

import argparse
import time
import brainflow
import numpy as np

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
from brainflow.data_filter import DataFilter, FilterTypes, AggOperations, WindowOperations, DetrendOperations

# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
nfft = DataFilter.get_nearest_power_of_two(sampling_rate)
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
time.sleep(10)
data = board.get_board_data()
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(board_id)
# use first eeg channel for demo
# second channel of synthetic board is a sine wave at 10 Hz, should see big 'alpha'
eeg_channel = eeg_channels[1]

[2024-02-22 21:32:38.371] [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
}

# optional: detrend
DataFilter.detrend(data[eeg_channel], DetrendOperations.LINEAR.value)

psd = DataFilter.get_psd_welch(data[eeg_channel], nfft, nfft // 2, sampling_rate, WindowOperations.HANNING.value)
plt.plot(psd[1][:60], psd[0][:60])
plt.show()

# calc band power
alpha = DataFilter.get_band_power(psd, 7.0, 13.0)
beta = DataFilter.get_band_power(psd, 14.0, 30.0)
print("Alpha/Beta Ratio is: %f" % (alpha / beta))

Alpha/Beta Ratio is: 2697.854098