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 low_pass(self, data, parameter_list):
filter_data = []
for i in range(parameter_list[-1]):
DataFilter.perform_lowpass(data[i], parameter_list[1],
parameter_list[2], parameter_list[3],
parameter_list[4], 3)
filter_data.append(data[i])
filter_data = np.array(filter_data)
return filter_data
def filtering(signal, sf, chosen_channels):
for ch in chosen_channels:
copy_signal = copy.deepcopy(signal[ch])
DataFilter.perform_lowpass(copy_signal, sf, 50.0, 5,
FilterTypes.CHEBYSHEV_TYPE_1.value, 1)
DataFilter.perform_highpass(copy_signal, sf, 3.0, 4,
FilterTypes.BUTTERWORTH.value, 0)
signal[ch] = copy_signal
return signal
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 update(self):
# received_data, addr = sock.recvfrom(1024) # buffer size is 1024 bytes
# print("Received message: ", received_data)
data = self.board_shim.get_current_board_data(self.num_points)
if data[0:7, 0:250].shape == (7, 250):
for count, channel in enumerate(self.exg_channels):
# plot timeseries
DataFilter.perform_lowpass(
data[channel], BoardShim.get_sampling_rate(args.board_id),
high, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_highpass(
data[channel], BoardShim.get_sampling_rate(args.board_id),
low, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(
data[channel], BoardShim.get_sampling_rate(args.board_id),
50, 2, 8, FilterTypes.BUTTERWORTH.value, 0)
self.curves[count + 1].setData(data[channel][-1001:].tolist())
window = data[0:8, -250:] # input window
window = window - window[3, :] # Cz reference
x = np.vstack((window[0:3, :], window[4:, :]))
x = create_data(x, fs, 1, low, high, n_freqs, zeros,
length) # convert to PSD
x = np.reshape(x, (1, n_channels_ref, n_freqs))
x_csp = csp2.transform(x)
window = np.reshape(window, (1, window.shape[0], window.shape[1]))
x_raw_csp = csp1.transform(window)
inference = np.hstack((x_csp, x_raw_csp))
current_time = datetime.now()
current_time = current_time.strftime("%M:%S")
result.append(model.predict(inference)[0])
MESSAGE = str(model.predict(inference)[0])
MESSAGE = bytes(MESSAGE, 'utf-8')
sock.sendto(MESSAGE, (UDP_IP, UDP_PORT))
pygame.time.delay(100)
self.curves[0].setData(result[-1001:])
self.app.processEvents()
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():
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()
# demo how to read data as 2d numpy array
board = BoardShim(args.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 (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
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)
df = pd.DataFrame(np.transpose(data))
print('Data From the Board')
print(df.head())
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('before_processing.png')
# demo for data serialization
DataFilter.write_file(data, 'test.csv', 'w')
restored_data = DataFilter.read_file('test.csv')
restored_df = pd.DataFrame(np.transpose(restored_data))
print('Data From the File')
print(restored_df.head())
# demo how to perform signal processing
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)
df = pd.DataFrame(np.transpose(data))
print('Data After Processing')
print(df.head())
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('after_processing.png')
