1 import time
 2 import numpy as np
 3 import brainflow
 4 from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
 5 from brainflow.data_filter import DataFilter, FilterTypes, AggOperations
 6 
 7 def main ():
 8     BoardShim.enable_dev_board_logger ()
 9 
10     # use synthetic board for demo
11     #GET PARAMS
12     params = BrainFlowInputParams ()
13     board = BoardShim (BoardIds.SYNTHETIC_BOARD.value, params)
14     board.prepare_session ()
15     board.start_stream ()
16     BoardShim.log_message (LogLevels.LEVEL_INFO.value, 'start sleeping in the main thread')
17     time.sleep (10)
18     data = board.get_current_board_data (20) # get 20 latest data points dont remove them from internal buffer
19     board.stop_stream ()
20     board.release_session ()
21 
22     eeg_channels = BoardShim.get_eeg_channels (BoardIds.SYNTHETIC_BOARD.value)
23     # demo for downsampling, it just aggregates data
24     #DataFilter to perform signal processing, it calls methods from underlying DataHandler library
25     for count, channel in enumerate (eeg_channels):
26         print ('Original data for channel %d:' % channel)
27         print (data[channel])
28         if count == 0:
29             downsampled_data = DataFilter.perform_downsampling (data[channel], 3, AggOperations.MEDIAN.value)
30         elif count == 1:
31             downsampled_data = DataFilter.perform_downsampling (data[channel], 2, AggOperations.MEAN.value)
32         else:
33             downsampled_data = DataFilter.perform_downsampling (data[channel], 2, AggOperations.EACH.value)
34         print ('Downsampled data for channel %d:' % channel)
35         print (downsampled_data)
36 
37 
38 if __name__ == "__main__":
39     main ()