def initialize_board(name='SYNTHETIC',port = None):
if name == 'SYNTHETIC':
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.accel_channels = BoardShim.get_accel_channels(board_id)
elif name == 'OPENBCI':
board_id = BoardIds.CYTON_DAISY_BOARD.value
params = BrainFlowInputParams()
params.serial_port = port
board_id = BoardIds.CYTON_DAISY_BOARD.value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.accel_channels = BoardShim.get_accel_channels(board_id)
print('Must have OpenBCI GUI open to work... (as port is not opened by Brainflow)')
board.prepare_session()
return board
def _get_source_info(self):
""" Gets board-specific information from the Brainflow library
Returns:
eeg_channels
sfreq
channel_names
"""
if self.board_type == 'synthetic':
eeg_channels = BoardShim.get_eeg_channels(
BoardIds.SYNTHETIC_BOARD.value)
sfreq = BoardShim.get_sampling_rate(BoardIds.SYNTHETIC_BOARD.value)
channel_names = [
'T7', 'CP5', 'FC5', 'C3', 'C4', 'FC6', 'CP6', 'T8'
]
elif self.board_type == 'cyton':
eeg_channels = BoardShim.get_eeg_channels(
BoardIds.CYTON_BOARD.value)
sfreq = BoardShim.get_sampling_rate(BoardIds.CYTON_BOARD.value)
channel_names = OPENBCI_STANDARD[:9]
elif self.board_type == 'daisy':
eeg_channels = BoardShim.get_eeg_channels(
BoardIds.CYTON_DAISY_BOARD.value)
sfreq = BoardShim.get_sampling_rate(
BoardIds.CYTON_DAISY_BOARD.value)
channel_names = OPENBCI_STANDARD
return [eeg_channels, sfreq, channel_names]
def main():
BoardShim.enable_dev_board_logger()
# 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()
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', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg']
ch_names = ['T7', 'CP5', 'FC5', 'C3', 'C4', 'FC6', 'CP6', 'T8']
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=True)
plt.savefig('psd.png')
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
nfft = DataFilter.get_nearest_power_of_two(sampling_rate)
data = board.get_board_data()
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(board_id)
# second eeg channel of synthetic board is a sine wave at 10Hz, should see huge alpha
eeg_channel = eeg_channels[1]
# optional detrend
DataFilter.detrend(data[eeg_channel], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data[eeg_channel], nfft, nfft // 2,
sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_alpha = DataFilter.get_band_power(psd, 7.0, 13.0)
band_power_beta = DataFilter.get_band_power(psd, 14.0, 30.0)
print("alpha/beta:%f", band_power_alpha / band_power_beta)
# fail test if ratio is not smth we expect
if (band_power_alpha / band_power_beta < 100):
raise ValueError('Wrong Ratio')
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board = BoardShim(BoardIds.SYNTHETIC_BOARD.value, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
data = board.get_current_board_data(
20) # get 20 latest data points dont remove them from internal buffer
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(BoardIds.SYNTHETIC_BOARD.value)
# demo for downsampling, it just aggregates data
for count, channel in enumerate(eeg_channels):
print('Original data for channel %d:' % channel)
print(data[channel])
if count == 0:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 3, AggOperations.MEDIAN.value)
elif count == 1:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 2, AggOperations.MEAN.value)
else:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 2, AggOperations.EACH.value)
print('Downsampled data for channel %d:' % channel)
print(downsampled_data)
def load_demo(duration=1) -> mne.io.RawArray:
"""Loads and returns some demo data"""
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board = BoardShim(BoardIds.SYNTHETIC_BOARD.value, params)
board.prepare_session()
board.start_stream()
print("Waiting for board shim to generate data...")
time.sleep(duration)
print("Done!")
data = board.get_board_data()
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(BoardIds.SYNTHETIC_BOARD.value)
# Only pick 8 channels
eeg_channels = eeg_channels[:8]
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", "eeg", "eeg", "eeg", "eeg", "eeg", "eeg", "eeg"]
ch_names = ["T7", "CP5", "FC5", "C3", "C4", "FC6", "CP6", "T8"]
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)
return raw
def main(self):
self.board.prepare_session()
self.board.start_stream()
try:
nfft = DataFilter.get_nearest_power_of_two(self.sampling_rate)
eeg_channels = BoardShim.get_eeg_channels(self.board_id)
time.sleep(3)
signals = []
cv2_thread = threading.Thread(target=self.cv2_video_read_thread)
audio_thread = threading.Thread(target=self.audio_thread)
cv2_thread.start()
audio_thread.start()
self.player_is_playing = True
while self.player_is_playing:
signals.append(self.on_next(eeg_channels, nfft))
if len(signals) > 3:
signals.pop(0)
avg_signal = sum(signals) / len(signals)
self.positive_signal = avg_signal < signals[-1]
self.last_signal_delta = abs(avg_signal - signals[-1])
if signals[-1] > 9: # min positive signals
self.positive_signal = True
print(
f'up {self.last_signal_delta}' if avg_signal < signals[-1]
else f'down {self.last_signal_delta}') # enable it later
audio_thread.join()
cv2_thread.join()
except Exception as e:
print(e)
return
def main(self):
self.board.prepare_session()
self.board.start_stream()
try:
nfft = DataFilter.get_nearest_power_of_two(self.sampling_rate)
eeg_channels = BoardShim.get_eeg_channels(self.board_id)
time.sleep(3)
cv2_thread = threading.Thread(target=self.cv2_video_thread)
audio_thread = threading.Thread(target=self.audio_thread)
cv2_thread.start()
audio_thread.start()
self.player_is_playing = True
signal_freq_coeff = 1.1 # auto adjustable coefficient?
high_signal_freq_coeff = 1.5
data_log_file = open(f'log2-{time.time()}.csv', 'a')
print_bands = []
for channel in self.channels.keys():
print_bands.append(','.join(
[b.name for b in self.channels[channel]]))
data_log_file.write(
f'time,metrics_sum,signal,high_signal,{",".join(print_bands)}')
metrics_hist = []
while self.player_is_playing:
time.sleep(.3)
self.on_next(eeg_channels, nfft)
metrics_sum = 0.0
for metric in self.metrics:
metrics_sum += metric.get_metric()
metrics_hist.append(metrics_sum)
if len(metrics_hist) > 50:
metrics_hist.pop(0)
avg_metrics_hist = sum(metrics_hist) / len(metrics_hist)
self.positive_signal = avg_metrics_hist < metrics_sum * signal_freq_coeff
self.is_last_signal_delta_high = False
if self.positive_signal and avg_metrics_hist < metrics_sum * high_signal_freq_coeff:
self.is_last_signal_delta_high = True
print(
f'{self.positive_signal} {avg_metrics_hist} < {metrics_sum*signal_freq_coeff}'
)
print_bands = []
for channel in self.channels.keys():
print_bands.append(','.join([
str(b.band_current_power)
for b in self.channels[channel]
]))
log_line = f'\n{time.asctime(time.gmtime(time.time()))},{metrics_sum},{self.positive_signal},{self.is_last_signal_delta_high},{",".join(print_bands)}'
data_log_file.write(log_line)
data_log_file.close()
audio_thread.join()
cv2_thread.join()
except Exception as e:
print(e)
self.player_is_playing = False
return
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
data = board.get_current_board_data(
DataFilter.get_nearest_power_of_two(sampling_rate))
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(board_id)
for count, channel in enumerate(eeg_channels):
# optional: subtract mean or detrend
psd = DataFilter.get_psd(data[channel], sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_alpha = DataFilter.get_band_power(psd, 7.0, 13.0)
band_power_beta = DataFilter.get_band_power(psd, 14.0, 30.0)
print("alpha/beta:%f", band_power_alpha / band_power_beta)
def main ():
BoardShim.enable_board_logger ()
DataFilter.enable_data_logger ()
MLModel.enable_ml_logger ()
parser = argparse.ArgumentParser ()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument ('--timeout', type = int, help = 'timeout for device discovery or connection', required = False, default = 0)
parser.add_argument ('--ip-port', type = int, help = 'ip port', required = False, default = 0)
parser.add_argument ('--ip-protocol', type = int, help = 'ip protocol, check IpProtocolType enum', required = False, default = 0)
parser.add_argument ('--ip-address', type = str, help = 'ip address', required = False, default = '')
parser.add_argument ('--serial-port', type = str, help = 'serial port', required = False, default = '')
parser.add_argument ('--mac-address', type = str, help = 'mac address', required = False, default = '')
parser.add_argument ('--other-info', type = str, help = 'other info', required = False, default = '')
parser.add_argument ('--streamer-params', type = str, help = 'streamer params', required = False, default = '')
parser.add_argument ('--serial-number', type = str, help = 'serial number', required = False, default = '')
parser.add_argument ('--board-id', type = int, help = 'board id, check docs to get a list of supported boards', required = True)
parser.add_argument ('--file', type = str, help = 'file', required = False, default = '')
args = parser.parse_args ()
params = BrainFlowInputParams ()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.serial_number = args.serial_number
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
params.timeout = args.timeout
params.file = args.file
board = BoardShim (args.board_id, params)
master_board_id = board.get_board_id ()
sampling_rate = BoardShim.get_sampling_rate (master_board_id)
board.prepare_session ()
board.start_stream (45000, args.streamer_params)
BoardShim.log_message (LogLevels.LEVEL_INFO.value, 'start sleeping in the main thread')
time.sleep (5) # recommended window size for eeg metric calculation is at least 4 seconds, bigger is better
data = board.get_board_data ()
board.stop_stream ()
board.release_session ()
eeg_channels = BoardShim.get_eeg_channels (int (master_board_id))
bands = DataFilter.get_avg_band_powers (data, eeg_channels, sampling_rate, True)
feature_vector = np.concatenate ((bands[0], bands[1]))
print(feature_vector)
# calc concentration
concentration_params = BrainFlowModelParams (BrainFlowMetrics.CONCENTRATION.value, BrainFlowClassifiers.KNN.value)
concentration = MLModel (concentration_params)
concentration.prepare ()
print ('Concentration: %f' % concentration.predict (feature_vector))
concentration.release ()
# calc relaxation
relaxation_params = BrainFlowModelParams (BrainFlowMetrics.RELAXATION.value, BrainFlowClassifiers.REGRESSION.value)
relaxation = MLModel (relaxation_params)
relaxation.prepare ()
print ('Relaxation: %f' % relaxation.predict (feature_vector))
relaxation.release ()
def __init__(self, data_idx=101, board=None):
self.data_idx = data_idx
self.board_id = 2
self.eeg_channels = BoardShim.get_eeg_channels(self.board_id)
self.ts_channels = BoardShim.get_timestamp_channel(self.board_id)
sfreq = BoardShim.get_sampling_rate(self.board_id)
self.freq = sfreq
ch_types = [
'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg',
'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg'
]
ch_names = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
'N', 'O', 'P'
]
self.info = mne.create_info(ch_names=ch_names,
sfreq=sfreq,
ch_types=ch_types)
if board is not None:
self.board = board
else:
params = BrainFlowInputParams()
params.serial_port = "/dev/cu.usbserial-DM01MTXZ"
board = BoardShim(self.board_id, params)
self.board.prepare_session()
self.board.start_stream()
def main ():
BoardShim.enable_dev_board_logger ()
# use synthetic board for demo
params = BrainFlowInputParams ()
board = BoardShim (BoardIds.SYNTHETIC_BOARD.value, params)
board.prepare_session ()
board.start_stream ()
BoardShim.log_message (LogLevels.LEVEL_INFO.value, 'start sleeping in the main thread')
time.sleep (10)
data = board.get_current_board_data (20) # get 20 latest data points dont remove them from internal buffer
board.stop_stream ()
board.release_session ()
# demo how to convert it to pandas DF and plot data
eeg_channels = BoardShim.get_eeg_channels (BoardIds.SYNTHETIC_BOARD.value)
df = pd.DataFrame (np.transpose (data))
print ('Data From the Board')
print (df.head (10))
# demo for data serialization using brainflow API, we recommend to use it instead pandas.to_csv()
DataFilter.write_file (data, 'test.csv', 'w') # use 'a' for append mode
restored_data = DataFilter.read_file ('test.csv')
restored_df = pd.DataFrame (np.transpose (restored_data))
print ('Data From the File')
print (restored_df.head (10))
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
data = board.get_board_data()
board.stop_stream()
board.release_session()
# demo how to convert it to pandas DF and plot data
eeg_channels = BoardShim.get_eeg_channels(board_id)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('before_processing.png')
# for demo apply different filters to different channels, in production choose one
for count, channel in enumerate(eeg_channels):
# filters work in-place
if count == 0:
DataFilter.perform_bandpass(data[channel],
BoardShim.get_sampling_rate(board_id),
15.0, 6.0, 4, FilterTypes.BESSEL.value,
0)
elif count == 1:
DataFilter.perform_bandstop(data[channel],
BoardShim.get_sampling_rate(board_id),
30.0, 1.0, 3,
FilterTypes.BUTTERWORTH.value, 0)
elif count == 2:
DataFilter.perform_lowpass(data[channel],
BoardShim.get_sampling_rate(board_id),
20.0, 5,
FilterTypes.CHEBYSHEV_TYPE_1.value, 1)
elif count == 3:
DataFilter.perform_highpass(data[channel],
BoardShim.get_sampling_rate(board_id),
3.0, 4, FilterTypes.BUTTERWORTH.value,
0)
elif count == 4:
DataFilter.perform_rolling_filter(data[channel], 3,
AggOperations.MEAN.value)
else:
DataFilter.remove_environmental_noise(
data[channel], BoardShim.get_sampling_rate(board_id),
NoiseTypes.FIFTY.value)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('after_processing.png')
def getEGG_Data(self): self.__board.stop_stream() data = self.__board.get_board_data() eeg_channels = BoardShim.get_eeg_channels(self.__board_ID) eeg_data = data[eeg_channels] return eeg_data
def main(self):
self.board.prepare_session()
self.board.start_stream()
try:
nfft = DataFilter.get_nearest_power_of_two(self.sampling_rate)
eeg_channels = BoardShim.get_eeg_channels(self.board_id)
time.sleep(3)
cv2_thread = threading.Thread(target=self.cv2_video_thread)
audio_thread = threading.Thread(target=self.audio_thread)
cv2_thread.start()
audio_thread.start()
self.player_is_playing = True
signal_freq_coeff = .5 # auto adjustable coefficient?
high_signal_freq_coeff = .15
data_log_file = open(f'log-{time.time()}.csv', 'a')
while self.player_is_playing:
time.sleep(.1)
bands_signals = self.on_next(eeg_channels, nfft)
positive_signals_sum = 0.0
negative_signals_sum = 0.0
for i in bands_signals.keys():
if bands_signals[i] > 0:
positive_signals_sum += bands_signals[i]
else:
negative_signals_sum += bands_signals[i]
self.positive_signal = abs(
negative_signals_sum
) < positive_signals_sum * signal_freq_coeff
self.is_last_signal_delta_high = False
if self.positive_signal and positive_signals_sum * high_signal_freq_coeff > abs(
negative_signals_sum):
self.is_last_signal_delta_high = True
print_bands = []
for proto in self.protocol:
print_bands.append(
f'{eeg_channels[proto.channel_inx]},' + ",".join(
[str(b.band_current_power) for b in proto.bands]))
log_line = f'\n{time.asctime(time.gmtime(time.time()))},{positive_signals_sum},{negative_signals_sum},{self.positive_signal},{self.is_last_signal_delta_high},{",".join(print_bands)}'
data_log_file.write(log_line)
data_log_file.close()
audio_thread.join()
cv2_thread.join()
except Exception as e:
print(e)
self.player_is_playing = False
return
def __init__(
self,
board_name: str,
serial_port: str = "",
mac_address: str = "",
ip_address: str = "",
ip_port: int = 0,
ip_protocol: int = 0,
other_info: str = "",
timeout: int = 0,
serial_number: str = "",
board_file: str = "",
):
self.board_name = board_name
if not valid_boardname(board_name):
raise BoardException(
f"Invalid board name in config: {self.board_name}")
else:
self.board_id = BoardIds[self.board_name].value
# Get and set vars with basic info about the board:
self.sample_rate = BoardShim.get_sampling_rate(self.board_id)
try:
self.channel_names = BoardShim.get_eeg_names(self.board_id)
except Exception:
self.channel_names = []
self.channel_count = len(BoardShim.get_eeg_channels(self.board_id))
# Prepare the board params object:
self.params = BrainFlowInputParams()
# Load params into the object from init function args:
self.params.serial_port = serial_port
self.params.mac_address = mac_address
self.params.ip_address = ip_address
self.params.ip_port = ip_port
self.params.ip_protocol = ip_protocol
self.params.other_info = other_info
self.params.timeout = timeout
self.params.serial_number = serial_number
self.params.file = board_file
# Construct the board object:
try:
self.board = BoardShim(self.board_id, self.params)
except Exception as e:
raise BoardException(
f"Failed to instantiate board object for {self.board_name}" +
str(e))
# Initial values:
self.is_ready = False
self.is_recording = False
# Self-check via our 'ping' function:
self.ping()
def main ():
parser = argparse.ArgumentParser ()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument ('--ip-port', type = int, help = 'ip port', required = False, default = 0)
parser.add_argument ('--ip-protocol', type = int, help = 'ip protocol, check IpProtocolType enum', required = False, default = 0)
parser.add_argument ('--ip-address', type = str, help = 'ip address', required = False, default = '')
parser.add_argument ('--serial-port', type = str, help = 'serial port', required = False, default = '')
parser.add_argument ('--mac-address', type = str, help = 'mac address', required = False, default = '')
parser.add_argument ('--other-info', type = str, help = 'other info', required = False, default = '')
parser.add_argument ('--board-id', type = int, help = 'board id, check docs to get a list of supported boards', required = True)
parser.add_argument ('--log', action = 'store_true')
args = parser.parse_args ()
params = BrainFlowInputParams ()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
if (args.log):
BoardShim.enable_dev_board_logger ()
else:
BoardShim.disable_board_logger ()
board = BoardShim (args.board_id, params)
board.prepare_session ()
# disable 2nd channel for cyton use real board to check it, emulator ignores commands
if args.board_id == brainflow.board_shim.BoardIds.CYTON_BOARD.value:
board.config_board ('x2100000X')
board.start_stream ()
time.sleep (10)
data = board.get_board_data ()
board.stop_stream ()
board.release_session ()
eeg_channels = BoardShim.get_eeg_channels (args.board_id)
for count, channel in enumerate (eeg_channels):
if count == 0:
DataFilter.perform_bandpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 15.0, 6.0, 4, FilterTypes.BESSEL.value, 0)
elif count == 1:
DataFilter.perform_bandstop (data[channel], BoardShim.get_sampling_rate (args.board_id), 5.0, 1.0, 3, FilterTypes.BUTTERWORTH.value, 0)
elif count == 2:
DataFilter.perform_lowpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 9.0, 5, FilterTypes.CHEBYSHEV_TYPE_1.value, 1)
elif count == 3:
DataFilter.perform_highpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 3.0, 4, FilterTypes.BUTTERWORTH.value, 0)
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(20)
data = board.get_board_data()
board.stop_stream()
board.release_session()
# demo how to convert it to pandas DF and plot data
eeg_channels = BoardShim.get_eeg_channels(board_id)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('before_processing.png')
# demo for denoising, apply different methods to different channels for demo
for count, channel in enumerate(eeg_channels):
# first of all you can try simple moving median or moving average with different window size
if count == 0:
DataFilter.perform_rolling_filter(data[channel], 3,
AggOperations.MEAN.value)
elif count == 1:
DataFilter.perform_rolling_filter(data[channel], 3,
AggOperations.MEDIAN.value)
# if methods above dont work for your signal you can try wavelet based denoising
# feel free to try different functions and decomposition levels
elif count == 2:
DataFilter.perform_wavelet_denoising(data[channel], 'db6', 3)
elif count == 3:
DataFilter.perform_wavelet_denoising(data[channel], 'bior3.9', 3)
elif count == 4:
DataFilter.perform_wavelet_denoising(data[channel], 'sym7', 3)
elif count == 5:
# with synthetic board this one looks like the best option, but it depends on many circumstances
DataFilter.perform_wavelet_denoising(data[channel], 'coif3', 3)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('after_processing.png')
def get_eeg_channels(board_id):
eeg_channels = BoardShim.get_eeg_channels(board_id)
# optional: filter some channels we dont want to consider
try:
eeg_names = BoardShim.get_eeg_names(board_id)
selected_channels = list()
# blacklisted_channels = {'O1', 'O2'}
blacklisted_channels = set()
for i, channel in enumerate(eeg_names):
if not channel in blacklisted_channels:
selected_channels.append(eeg_channels[i])
eeg_channels = selected_channels
except Exception as e:
print(str(e))
print('channels to use: %s' % str(eeg_channels))
return eeg_channels
def eeg_signals():
#获取原始数据，根据采样率大小取出1s的数据
eeg_data = board.get_current_board_data(sampling_rate)[0:9]
# 带通滤波处理（0.5-50），中心频率25.25，带宽49.5
eeg_channels = BoardShim.get_eeg_channels(0)
for count, channel in enumerate(eeg_channels):
eeg_data[channel] = eeg_data[channel] - np.average(eeg_data[channel])
DataFilter.perform_bandpass(eeg_data[channel],
BoardShim.get_sampling_rate(2), 25.25,
49.5, 3, FilterTypes.BESSEL.value, 0)
eeg_data = eeg_data[1:9]
eeg_data = np.array([eeg_data])
pca = UnsupervisedSpatialFilter(PCA(8), average=False)
eeg_data = pca.fit_transform(eeg_data)
eeg_data = eeg_data[0]
return eeg_data
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
data = board.get_current_board_data(
DataFilter.get_nearest_power_of_two(sampling_rate))
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(board_id)
# demo for transforms
for count, channel in enumerate(eeg_channels):
print('Original data for channel %d:' % channel)
print(data[channel])
# demo for wavelet transforms
# wavelet_coeffs format is[A(J) D(J) D(J-1) ..... D(1)] where J is decomposition level, A - app coeffs, D - detailed coeffs
# lengths array stores lengths for each block
wavelet_coeffs, lengths = DataFilter.perform_wavelet_transform(
data[channel], 'db5', 3)
app_coefs = wavelet_coeffs[0:lengths[0]]
detailed_coeffs_first_block = wavelet_coeffs[lengths[0]:lengths[1]]
# you can do smth with wavelet coeffs here, for example denoising works via thresholds
# for wavelets coefficients
restored_data = DataFilter.perform_inverse_wavelet_transform(
(wavelet_coeffs, lengths), data[channel].shape[0], 'db5', 3)
print('Restored data after wavelet transform for channel %d:' %
channel)
print(restored_data)
# demo for fft, len of data must be a power of 2
fft_data = DataFilter.perform_fft(data[channel],
WindowFunctions.NO_WINDOW.value)
# len of fft_data is N / 2 + 1
restored_fft_data = DataFilter.perform_ifft(fft_data)
print('Restored data after fft for channel %d:' % channel)
print(restored_fft_data)
def main():
BoardShim.enable_board_logger()
DataFilter.enable_data_logger()
MLModel.enable_ml_logger()
params = BrainFlowInputParams()
board = BoardShim(BoardIds.BRAINBIT_BOARD.value, params)
master_board_id = board.get_board_id()
sampling_rate = BoardShim.get_sampling_rate(master_board_id)
board.prepare_session()
board.start_stream(45000, '')
eeg_channels = BoardShim.get_eeg_channels(int(master_board_id))
while True:
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(
5
) # recommended window size for eeg metric calculation is at least 4 seconds, bigger is better
data = board.get_board_data()
bands = DataFilter.get_avg_band_powers(data, eeg_channels,
sampling_rate, True)
feature_vector = np.concatenate((bands[0], bands[1]))
print(feature_vector)
# calc concentration
concentration_params = BrainFlowModelParams(
BrainFlowMetrics.CONCENTRATION.value,
BrainFlowClassifiers.KNN.value)
concentration = MLModel(concentration_params)
concentration.prepare()
print('Concentration: %f' % concentration.predict(feature_vector))
concentration.release()
# calc relaxation
relaxation_params = BrainFlowModelParams(
BrainFlowMetrics.RELAXATION.value,
BrainFlowClassifiers.REGRESSION.value)
relaxation = MLModel(relaxation_params)
relaxation.prepare()
print('Relaxation: %f' % relaxation.predict(feature_vector))
relaxation.release()
board.stop_stream()
board.release_session()
def start_recording(total_sec, max_samples, params, args):
board = BoardShim(args.board_id, params)
board.prepare_session()
# board.start_stream () # use this for default options
board.start_stream(max_samples, args.streamer_params)
time.sleep(total_sec)
# data = board.get_current_board_data (256) # get latest 256 packages or less, doesnt remove them from internal buffer
data = board.get_board_data(
) # get all data and remove it from internal buffer
timestamp_channel = board.get_timestamp_channel(board_id=0)
board.stop_stream()
board.release_session()
# demo how to convert it to pandas DF and plot data
eeg_channels = BoardShim.get_eeg_channels(args.board_id)
print(eeg_channels)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('data/before_processing.png')
# for demo apply different filters to different channels, in production choose one
for count, channel in enumerate(eeg_channels):
DataFilter.perform_lowpass(data[channel],
BoardShim.get_sampling_rate(args.board_id),
30, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_highpass(data[channel],
BoardShim.get_sampling_rate(args.board_id),
5, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel],
BoardShim.get_sampling_rate(args.board_id),
50, 2, 8, FilterTypes.BUTTERWORTH.value, 0)
df = pd.DataFrame(np.transpose(
data[:, 1000:])) # Usable after 1000 given order of filters are 3
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('data/after_processing.png')
return data, timestamp_channel
def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
board = BoardShim(board_id, params)
board.prepare_session()
board.start_stream()
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(10)
data = board.get_board_data()
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(board_id)
bands = DataFilter.get_avg_band_powers(data, eeg_channels, sampling_rate,
True)
print("avg band powers : %s" % str(bands[0]))
print("stddev band powers : %s" % str(bands[1]))
def main(i):
board_id = BoardIds.SYNTHETIC_BOARD.value
eeg_channels = BoardShim.get_eeg_channels(board_id)
timestamp = BoardShim.get_timestamp_channel(board_id)
style.use('fivethirtyeight')
plt.title("Live EEG Datastream from Brainflow", fontsize=15)
plt.ylabel("Data in millivolts", fontsize=15)
plt.xlabel("\nTime", fontsize=10)
data = DataFilter.read_file('data.csv')
eegdf = pd.DataFrame(np.transpose(data[eeg_channels, timestamp]))
#timedf = pd.DataFrame(np.transpose(data[timestamp])) #to keep it simple, making another dataframe for the timestamps to access later
eegdf_col_names = ["ch1","ch2","ch3","ch4","ch5","ch6","ch7","ch8","ch9","ch10","ch11","ch12","ch13","ch14","ch15","ch16"]
eegdf.columns = eegdf_col_names
print("EEG Dataframe")
print(eegdf) #easy way to check what data is being streamed and if program is working
#print(eegdf)
eeg1 = eegdf.iloc[:, 0].values #I am using OpenBCI Ganglion board, so only have four channels.
eeg2 = eegdf.iloc[:, 1].values
eeg3 = eegdf.iloc[:, 2].values
eeg4 = eegdf.iloc[:, 3].values
timex= eegdf.iloc[:, 15].values #timestamps
#print(timex) #use this to see what the UNIX timestamps look like
print("EEG Channel 1")
print(eeg1)
#print("Time DF")
#print(timedf)
print("Timestamp")
print(timex)
"""plt.cla()
def main():
'''
Connect to the Brainstorm
'''
# Authentication
username = '******'
password = ''
## Set Connection Details
# brainstorm = brainsatplay.Brainstorm('https://localhost') # Local
brainstorm = brainsatplay.Brainstorm() # Deployed Server
## Connect
res = brainstorm.connect(username,
password) # All optional (defaults to guest)
'''
Subscribe to a Particular Game
'''
# # Connection Settings
# appname = 'brainstorm'
# devices = []
# props = ['raw','times','sps','deviceType','format','eegChannelTags']
# sessionid = None
# spectating = False # Spectate to view data without sending it
# res = brainstorm.getSessions(appname)
# if res['msg'] != 'appNotFound':
# sessionid = res['sessions'][0]['id']
# else:
# res = brainstorm.createSession(appname, devices, props)
# sessionid = res['sessionInfo']['id']
# # Handle Data from Subscribed Games
# def newData(json):
# for user in json['userData']:
# name = user['username']
# print('Data for {}'.format(name))
# res = brainstorm.subscribeToSession(sessionid,spectating, newData)
'''
Stream your Data
'''
# Setup Brainflow
params = BrainFlowInputParams()
board_id = BoardIds['SYNTHETIC_BOARD'].value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.eeg_names = BoardShim.get_eeg_names(board_id)
board.prepare_session()
board.start_stream(num_samples=450000)
# Handle CTRL-C Exit
def onStop():
board.stop_stream()
board.release_session()
loopCount = 0
# Start Stream Loop
def streamLoop():
pass_data = []
rate = DataFilter.get_nearest_power_of_two(board.rate)
data = board.get_board_data()
t = data[board.time_channel]
data = data[board.eeg_channels]
for entry in data:
pass_data.append((entry).tolist())
data = {}
data['raw'] = pass_data
data['times'] = t.tolist()
# Send Metadata on First Loop
if loopCount == 0:
data['sps'] = board.rate
data['deviceType'] = 'eeg'
data['format'] = 'brainflow'
tags = []
for i, channel in enumerate(board.eeg_channels):
tags.append({
'ch': channel - 1,
'tag': board.eeg_names[i],
'analyze': True
})
data['eegChannelTags'] = tags
return data
res = brainstorm.startStream(streamLoop, onStop)
def __init__(self, board, board_id):
threading.Thread.__init__(self)
self.eeg_channels = BoardShim.get_eeg_channels(board_id)
self.sampling_rate = BoardShim.get_sampling_rate(board_id)
self.keep_alive = True
self.board = board
def main():
parser = argparse.ArgumentParser()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument('--ip-port',
type=int,
help='ip port',
required=False,
default=0)
parser.add_argument('--ip-protocol',
type=int,
help='ip protocol, check IpProtocolType enum',
required=False,
default=0)
parser.add_argument('--ip-address',
type=str,
help='ip address',
required=False,
default='')
parser.add_argument('--serial-port',
type=str,
help='serial port',
required=False,
default='')
parser.add_argument('--mac-address',
type=str,
help='mac address',
required=False,
default='')
parser.add_argument('--other-info',
type=str,
help='other info',
required=False,
default='')
parser.add_argument('--streamer-params',
type=str,
help='other info',
required=False,
default='')
parser.add_argument(
'--board-id',
type=int,
help='board id, check docs to get a list of supported boards',
required=True)
parser.add_argument('--log', action='store_true')
parser.add_argument('--run-time',
type=int,
help='run time in sec',
required=True)
args = parser.parse_args()
params = BrainFlowInputParams()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
if (args.log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
# for streaming board need to use master board id
master_board_id = args.board_id
if args.board_id == BoardIds.STREAMING_BOARD.value:
master_board_id = int(params.other_info)
board = BoardShim(args.board_id, params)
board.prepare_session()
buffer_size = int(
BoardShim.get_sampling_rate(master_board_id) * args.run_time *
1.2) # + 20% for safety
board.start_stream(buffer_size, args.streamer_params)
time.sleep(args.run_time)
board.stop_stream()
data = board.get_board_data()
board.release_session()
if master_board_id in (BoardIds.CYTON_BOARD.value,
BoardIds.CYTON_WIFI_BOARD.value,
BoardIds.GANGLION_WIFI_BOARD.value):
bytes_per_package = 33
elif master_board_id in (BoardIds.CYTON_DAISY_BOARD,
BoardIds.CYTON_DAISY_WIFI_BOARD.value):
bytes_per_package = 66
elif master_board_id == BoardIds.SYNTHETIC_BOARD.value:
bytes_per_package = 104
elif master_board_id == BoardIds.NOVAXR_BOARD.value:
bytes_per_package = 72
else:
raise ValueError('unsupported board')
total_bytes_received = bytes_per_package * data.shape[1]
packages_per_sec = float(data.shape[1]) / float(args.run_time)
timestamp_channel = BoardShim.get_timestamp_channel(master_board_id)
timestamp_array = data[timestamp_channel]
time_diff_array = list()
for i in range(0, timestamp_array.size - 1):
time_diff_array.append(timestamp_array[i + 1] - timestamp_array[i])
package_num_channel = BoardShim.get_package_num_channel(master_board_id)
package_num_array = data[package_num_channel]
lost_packages = 0
expected = 0
cur_id = 0
while cur_id < package_num_array.size:
if expected == 256:
expected = 0
if package_num_array[cur_id] != expected:
BoardShim.log_message(
LogLevels.LEVEL_WARN.value,
'package loss detected: position %d package_num value %d expected value %d'
% (cur_id, package_num_array[cur_id], expected))
lost_packages = lost_packages + 1
else:
cur_id = cur_id + 1
expected = expected + 1
package_loss = (lost_packages / data.shape[1]) * 100
BoardShim.log_message(LogLevels.LEVEL_INFO.value, '\nResults:\n')
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'package loss percent %f' % package_loss)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'average time delta %f' % statistics.mean(time_diff_array))
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'std deviation of time delta %f' % statistics.pstdev(time_diff_array))
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total packages received %d' % data.shape[1])
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'packages per sec %f' % packages_per_sec)
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total bytes received %d' % total_bytes_received)
eeg_channels = BoardShim.get_eeg_channels(master_board_id)
emg_channels = BoardShim.get_emg_channels(master_board_id)
total_channels = eeg_channels
# for cyton/ganglion eeg_channels and emg_channels are the same array because we can not split it
# for novaxr its 2 different arrays, join them
for ch in emg_channels:
if ch not in total_channels:
total_channels.append(ch)
df = pd.DataFrame(np.transpose(data))
df[total_channels].to_csv('eeg_emg_data.csv')
df.to_csv('all_data.csv')
plt.figure()
df[total_channels].plot(subplots=True)
plt.show()
class OpenBCIInterface:
def __init__(self,
serial_port='COM5',
board_id=0,
log='store_true',
streamer_params='',
ring_buffer_size=45000): # default board_id 2 for Cyton
params = BrainFlowInputParams()
params.serial_port = serial_port
params.ip_port = 0
params.mac_address = ''
params.other_info = ''
params.serial_number = ''
params.ip_address = ''
params.ip_protocol = 0
params.timeout = 0
params.file = ''
self.streamer_params = streamer_params
self.ring_buffer_size = ring_buffer_size
self.board_id = board_id
self.info_eeg = None
self.info_aux = None
self.outlet_eeg = None
self.outlet_aux = None
if (log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
self.board = BoardShim(board_id, params)
def start_sensor(self):
# tell the sensor to start sending frames
self.board.prepare_session()
print('OpenBCIInterface: connected to sensor')
print(self.board.get_board_id())
try:
self.board.start_stream(self.ring_buffer_size,
self.streamer_params)
self.infor_test()
except brainflow.board_shim.BrainFlowError:
print('OpenBCIInterface: Board is not ready.')
def process_frames(self):
# return one or more frames of the sensor
frames = self.board.get_board_data()
return frames
def stop_sensor(self):
try:
self.board.stop_stream()
print('OpenBCIInterface: stopped streaming.')
self.board.release_session()
print('OpenBCIInterface: released session.')
except brainflow.board_shim.BrainFlowError as e:
print(e)
def create_lsl(self,
name='OpenBCI_Cyton_8',
type='EEG',
channel_count=8,
nominal_srate=250.0,
channel_format='float32',
source_id='Cyton_0'):
self.info_eeg = StreamInfo(name=name,
type=type,
channel_count=channel_count,
nominal_srate=nominal_srate,
channel_format=channel_format,
source_id='')
chns = self.info_eeg.desc().append_child('channels')
self.labels = ['Fp1', 'Fp2', 'C3', 'C4', 'T5', 'T6', 'O1', 'O2']
for label in self.labels:
ch = chns.append_child("channel")
ch.append_child_value('label', label)
ch.append_child_value('unit', 'microvolts')
ch.append_child_value('type', 'EEG')
self.info_eeg.desc().append_child_value('manufacturer', 'OpenBCI Inc.')
self.outlet_eeg = StreamOutlet(self.info_eeg)
print("--------------------------------------\n" + \
"LSL Configuration: \n" + \
" Stream 1: \n" + \
" Name: " + name + " \n" + \
" Type: " + type + " \n" + \
" Channel Count: " + str(channel_count) + "\n" + \
" Sampling Rate: " + str(nominal_srate) + "\n" + \
" Channel Format: " + channel_format + " \n" + \
" Source Id: " + source_id + " \n")
def push_sample(self, samples):
self.outlet_eeg.push_sample(samples)
def infor_test(self):
print(self.board.get_eeg_names(self.board_id))
print(self.board.get_sampling_rate(self.board_id))
print(self.board.get_board_id())
print(self.board.get_package_num_channel(self.board_id))
print(self.board.get_timestamp_channel(self.board_id))
print(self.board.get_eeg_channels(self.board_id))
print(self.board.get_accel_channels(self.board_id))
print(self.board.get_marker_channel(self.board_id))
print(self.board.get_other_channels(self.board_id))
print(self.board.get_analog_channels(self.board_id))
print(self.board.get_other_channels(self.board_id))
def main():
parser = argparse.ArgumentParser()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument('--ip-port',
type=int,
help='ip port',
required=False,
default=0)
parser.add_argument('--ip-protocol',
type=int,
help='ip protocol, check IpProtocolType enum',
required=False,
default=0)
parser.add_argument('--ip-address',
type=str,
help='ip address',
required=False,
default='')
parser.add_argument('--serial-port',
type=str,
help='serial port',
required=False,
default='')
parser.add_argument('--mac-address',
type=str,
help='mac address',
required=False,
default='')
parser.add_argument('--other-info',
type=str,
help='other info',
required=False,
default='')
parser.add_argument('--streamer-params',
type=str,
help='other info',
required=False,
default='')
parser.add_argument(
'--board-id',
type=int,
help='board id, check docs to get a list of supported boards',
required=True)
parser.add_argument('--log', action='store_true')
parser.add_argument('--run-time',
type=int,
help='run time for one iteration in sec',
required=True)
parser.add_argument('--num-iters',
type=int,
help='number of iterations',
default=1)
parser.add_argument(
'--channels',
type=str,
help='channels to plot in format 0,1,2 by default plot all channels',
default=None)
parser.add_argument('--config-file',
type=str,
help='file with strings to send to device',
default=None)
args = parser.parse_args()
params = BrainFlowInputParams()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
if (args.log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
# for streaming board need to use master board id
master_board_id = args.board_id
if args.board_id == BoardIds.STREAMING_BOARD.value:
master_board_id = int(params.other_info)
board = BoardShim(args.board_id, params)
board.prepare_session()
if args.config_file:
with open(args.config_file) as file:
lines = file.readlines()
for line in lines:
board.config_board(line)
buffer_size = int(
BoardShim.get_sampling_rate(master_board_id) * args.run_time *
1.2) # + 20% for safety
if master_board_id in (BoardIds.CYTON_BOARD.value,
BoardIds.CYTON_WIFI_BOARD.value,
BoardIds.GANGLION_WIFI_BOARD.value):
bytes_per_package = 33
elif master_board_id in (BoardIds.CYTON_DAISY_BOARD,
BoardIds.CYTON_DAISY_WIFI_BOARD.value):
bytes_per_package = 66
elif master_board_id == BoardIds.SYNTHETIC_BOARD.value:
bytes_per_package = 104
elif master_board_id == BoardIds.NOVAXR_BOARD.value:
bytes_per_package = 72
else:
raise ValueError('unsupported board')
timestamp_channel = BoardShim.get_timestamp_channel(master_board_id)
package_num_channel = BoardShim.get_package_num_channel(master_board_id)
try:
cur_id = 0
for i in range(args.num_iters):
# wait for an input
input('Press Enter to continue...')
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'\nRunning iteration %d/%d\n' % (i, args.num_iters))
# start stream and get data
board.start_stream(buffer_size, args.streamer_params)
time.sleep(args.run_time)
board.stop_stream()
data = board.get_board_data()
if data.shape[1] == 0:
BoardShim.log_message(LogLevels.LEVEL_WARN.value,
'\nNo data received!\n')
continue
# calculate some metrics
total_bytes_received = bytes_per_package * data.shape[1]
packages_per_sec = float(data.shape[1]) / float(args.run_time)
timestamp_array = data[timestamp_channel]
time_diff_array = list()
for i in range(0, timestamp_array.size - 1):
time_diff_array.append(timestamp_array[i + 1] -
timestamp_array[i])
package_num_array = data[package_num_channel]
lost_packages = 0
expected = package_num_array[0]
while cur_id < package_num_array.size:
if expected == 256:
expected = 0
if package_num_array[cur_id] != expected:
BoardShim.log_message(
LogLevels.LEVEL_WARN.value,
'package loss detected: position %d package_num value %d expected value %d'
% (cur_id, package_num_array[cur_id], expected))
lost_packages = lost_packages + 1
else:
cur_id = cur_id + 1
expected = expected + 1
package_loss = (lost_packages / data.shape[1]) * 100
# provide results for iteration
BoardShim.log_message(LogLevels.LEVEL_INFO.value, '\nResults:\n')
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'package loss percent %f' % package_loss)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'average time delta %f' % statistics.mean(time_diff_array))
BoardShim.log_message(
LogLevels.LEVEL_INFO.value, 'std deviation of time delta %f' %
statistics.pstdev(time_diff_array))
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total packages received %d' % data.shape[1])
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'packages per sec %f' % packages_per_sec)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'total bytes received %d' % total_bytes_received)
# plot data
eeg_channels = BoardShim.get_eeg_channels(master_board_id)
emg_channels = BoardShim.get_emg_channels(master_board_id)
total_channels = list()
if args.channels is not None:
selected_channels = [int(x) for x in args.channels.split(',')]
temp_channels = eeg_channels
for ch in emg_channels:
if ch not in temp_channels:
temp_channels.append(ch)
for i in range(len(temp_channels)):
if i in selected_channels:
total_channels.append(temp_channels[i])
else:
# for cyton/ganglion eeg_channels and emg_channels are the same array because we can not split it
# for novaxr its 2 different arrays, join them
total_channels = eeg_channels
for ch in emg_channels:
if ch not in total_channels:
total_channels.append(ch)
total_channels.append(timestamp_channel)
columns = list()
for i in range(len(total_channels) - 1):
columns.append('channel_%d' % (int(total_channels[i]) - 1))
columns.append('timestamp')
df = pd.DataFrame(np.transpose(data))
df.to_csv('all_data_%d.csv' % i)
df_to_plot = df[total_channels]
df_to_plot.columns = columns
df_to_plot.to_csv('selected_data_%d.csv' % i)
df_to_plot.plot(subplots=True, x='timestamp', style='.-')
plt.show()
finally:
# release session in the end
board.release_session()
