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 update(self):
data = self.board_shim.get_current_board_data(self.num_points)
avg_bands = [0, 0, 0, 0, 0]
for count, channel in enumerate(self.exg_channels):
# plot timeseries
DataFilter.detrend(data[channel], DetrendOperations.CONSTANT.value)
DataFilter.perform_bandpass(data[channel], self.sampling_rate, 30.0, 56.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel], self.sampling_rate, 50.0, 4.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel], self.sampling_rate, 60.0, 4.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
self.curves[count].setData(data[channel].tolist())
if data.shape[1] > self.psd_size:
# plot psd
psd_data = DataFilter.get_psd_welch(data[channel], self.psd_size, self.psd_size // 2, self.sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
lim = min(70, len(psd_data[0]))
self.psd_curves[count].setData(psd_data[1][0:lim].tolist(), psd_data[0][0:lim].tolist())
# plot bands
avg_bands[0] = avg_bands[0] + DataFilter.get_band_power(psd_data, 1.0, 4.0)
avg_bands[1] = avg_bands[1] + DataFilter.get_band_power(psd_data, 4.0, 8.0)
avg_bands[2] = avg_bands[2] + DataFilter.get_band_power(psd_data, 8.0, 13.0)
avg_bands[3] = avg_bands[3] + DataFilter.get_band_power(psd_data, 13.0, 30.0)
avg_bands[4] = avg_bands[4] + DataFilter.get_band_power(psd_data, 30.0, 50.0)
avg_bands = [int(x * 100 / len(self.exg_channels)) for x in avg_bands]
self.band_bar.setOpts(height=avg_bands)
self.app.processEvents()
def psd_welch(self, data, parameter_list):
feature_data = []
for i in range(parameter_list[-1]):
DataFilter.detrend(data[i], 1)
DataFilter.detrend(data[i], 2)
new_data = DataFilter.get_psd_welch(data[i], parameter_list[1], parameter_list[2], parameter_list[3],
parameter_list[4])
feature_data.append(new_data[0])
feature_data = np.array(feature_data)
return feature_data
def on_next(self, eeg_channels, nfft):
data = self.board.get_current_board_data(
max(self.sampling_rate, nfft) +
1) #get_board_data () we are taking ~1 sec data ~10 times a sec
for channel in self.channels.keys():
channel_data = data[channel]
DataFilter.detrend(channel_data, DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(
channel_data, nfft, nfft // 2, self.sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
for component in self.channels[channel]:
component.add_band_power_value(psd)
def on_next(self, eeg_channels, nfft):
data = self.board.get_current_board_data(
max(self.sampling_rate, nfft) +
1) #get_board_data () we are taking ~1 sec data ~10 times a sec
bands_sum = collections.defaultdict(float)
for channel in self.protocol:
channel_data = data[eeg_channels[channel.channel_inx]]
DataFilter.detrend(channel_data, DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(
channel_data, nfft, nfft // 2, self.sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
for band in channel.bands:
band.add_band_power_value(psd)
bands_sum[band.name] += band.get_signal()
return bands_sum
def on_next(self, eeg_channels, nfft):
time.sleep(.3)
data = self.board.get_current_board_data(
max(self.sampling_rate, nfft) +
1) #get_board_data () we are taking ~1 sec data ~3 times a sec
for channel in self.protocol:
channel_data = data[eeg_channels[channel.channel_inx]]
DataFilter.detrend(channel_data, DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(
channel_data, nfft, nfft // 2, self.sampling_rate,
WindowFunctions.BLACKMAN_HARRIS.value)
for band in channel.bands:
band.add_band_power_value(psd, 30)
#print(f'channel: {channel.channel_inx} positive_signals: {channel.get_positive_signals_count()} avg_power: {channel.get_avg_bands()}')
return sum([i.get_positive_signals_count() for i in self.protocol])
def update(self):
data = self.board_shim.get_current_board_data(self.num_points)
for count, channel in enumerate(self.exg_channels):
# plot timeseries
DataFilter.detrend(data[channel], DetrendOperations.CONSTANT.value)
DataFilter.perform_bandpass(data[channel], self.sampling_rate,
51.0, 100.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandpass(data[channel], self.sampling_rate,
51.0, 100.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel], self.sampling_rate,
50.0, 4.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel], self.sampling_rate,
60.0, 4.0, 2,
FilterTypes.BUTTERWORTH.value, 0)
self.curves[count].setData(data[channel].tolist())
self.app.processEvents()
