def main():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board = BoardShim(BoardIds.SYNTHETIC_BOARD.value, 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(
20) # get 20 latest data points dont remove them from internal buffer
board.stop_stream()
board.release_session()
eeg_channels = BoardShim.get_eeg_channels(BoardIds.SYNTHETIC_BOARD.value)
# demo for downsampling, it just aggregates data
for count, channel in enumerate(eeg_channels):
print('Original data for channel %d:' % channel)
print(data[channel])
if count == 0:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 3, AggOperations.MEDIAN.value)
elif count == 1:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 2, AggOperations.MEAN.value)
else:
downsampled_data = DataFilter.perform_downsampling(
data[channel], 2, AggOperations.EACH.value)
print('Downsampled data for channel %d:' % channel)
print(downsampled_data)
def down_sampling(signal, chosen_channels):
down_signal = []
for ch in chosen_channels:
copy_signal = copy.deepcopy(
signal[ch]) # ch 단위로 카피해야함.. (이상함..아무튼 그럼..)
down_signal.append(
DataFilter.perform_downsampling(copy_signal, 2,
AggOperations.MEAN.value))
down_signal = np.array(down_signal)
return down_signal
