def denoise(sample, num_channels=16):
for channel in range(num_channels):
DataFilter.perform_rolling_filter(sample[0][channel], 3,
AggOperations.MEDIAN.value)
DataFilter.perform_wavelet_denoising(sample[0][channel], 'db6', 3)
sample = sample / np.expand_dims(sample.std(axis=-1), axis=-1)
return sample
def wavelet_filter(self, data, parameter_list):
filter_data = []
for i in range(parameter_list[-1]):
# print(parameter_list[-1])
# print(i)
DataFilter.perform_wavelet_denoising(data[i], parameter_list[1],
parameter_list[2])
filter_data.append(data[i])
filter_data = np.array(filter_data)
return filter_data
def denoise_data_pre_raw(self, data, denoise_method):
for channel in self.eeg_info[0]:
if denoise_method == 'mean':
DataFilter.perform_rolling_filter(data[channel], 3,
AggOperations.MEAN.value)
elif denoise_method == 'median':
DataFilter.perform_rolling_filter(data[channel], 3,
AggOperations.MEDIAN.value)
else:
DataFilter.perform_wavelet_denoising(data[channel],
denoise_method, 3)
return data
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')