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_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 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 add_band_power_value(self, psd, max_size):
value = DataFilter.get_band_power(psd, self.band_range_min,
self.band_range_max)
self.band_current_power = value
self.power_values.append(value)
if (len(self.power_values) > max_size):
self.power_values.pop(0)
def add_band_power_value(self, psd):
value = DataFilter.get_band_power(psd, self.band_range_min,
self.band_range_max)
if self.precentile_power == 0.0 or value < self.precentile_power * self.signal_diviation_cut:
self._add_band_power_value(value)
def add_band_power_value(self, psd):
value = DataFilter.get_band_power(psd, self.band_range_min,
self.band_range_max)
self._add_band_power_value(value)
def bp_window(self, data, low, high, n_pads):
padded = np.pad(data[-self.num_points_partial:], n_pads)
psd = DataFilter.get_psd(padded, self.sampling_rate, WindowFunctions.BLACKMAN_HARRIS.value)
bp = DataFilter.get_band_power(psd, low, high)
return bp
