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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 ()
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)
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 (10))
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 (10))
# demo how to perform signal processing
for count, channel in enumerate (eeg_channels):
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):