def main ():
BoardShim.enable_board_logger ()
DataFilter.enable_data_logger ()
MLModel.enable_ml_logger ()
parser = argparse.ArgumentParser ()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
parser.add_argument ('--timeout', type = int, help = 'timeout for device discovery or connection', required = False, default = 0)
parser.add_argument ('--ip-port', type = int, help = 'ip port', required = False, default = 0)
parser.add_argument ('--ip-protocol', type = int, help = 'ip protocol, check IpProtocolType enum', required = False, default = 0)
parser.add_argument ('--ip-address', type = str, help = 'ip address', required = False, default = '')
parser.add_argument ('--serial-port', type = str, help = 'serial port', required = False, default = '')
parser.add_argument ('--mac-address', type = str, help = 'mac address', required = False, default = '')
parser.add_argument ('--other-info', type = str, help = 'other info', required = False, default = '')
parser.add_argument ('--streamer-params', type = str, help = 'streamer params', required = False, default = '')
parser.add_argument ('--serial-number', type = str, help = 'serial number', required = False, default = '')
parser.add_argument ('--board-id', type = int, help = 'board id, check docs to get a list of supported boards', required = True)
parser.add_argument ('--file', type = str, help = 'file', required = False, default = '')
args = parser.parse_args ()
params = BrainFlowInputParams ()
params.ip_port = args.ip_port
params.serial_port = args.serial_port
params.mac_address = args.mac_address
params.other_info = args.other_info
params.serial_number = args.serial_number
params.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
params.timeout = args.timeout
params.file = args.file
board = BoardShim (args.board_id, params)
master_board_id = board.get_board_id ()
sampling_rate = BoardShim.get_sampling_rate (master_board_id)
board.prepare_session ()
board.start_stream (45000, args.streamer_params)
BoardShim.log_message (LogLevels.LEVEL_INFO.value, 'start sleeping in the main thread')
time.sleep (5) # recommended window size for eeg metric calculation is at least 4 seconds, bigger is better
data = board.get_board_data ()
board.stop_stream ()
board.release_session ()
eeg_channels = BoardShim.get_eeg_channels (int (master_board_id))
bands = DataFilter.get_avg_band_powers (data, eeg_channels, sampling_rate, True)
feature_vector = np.concatenate ((bands[0], bands[1]))
print(feature_vector)
# calc concentration
concentration_params = BrainFlowModelParams (BrainFlowMetrics.CONCENTRATION.value, BrainFlowClassifiers.KNN.value)
concentration = MLModel (concentration_params)
concentration.prepare ()
print ('Concentration: %f' % concentration.predict (feature_vector))
concentration.release ()
# calc relaxation
relaxation_params = BrainFlowModelParams (BrainFlowMetrics.RELAXATION.value, BrainFlowClassifiers.REGRESSION.value)
relaxation = MLModel (relaxation_params)
relaxation.prepare ()
print ('Relaxation: %f' % relaxation.predict (feature_vector))
relaxation.release ()
def test_brainflow_knn (data):
print ('Test BrainFlow KNN')
params = BrainFlowModelParams (BrainFlowMetrics.CONCENTRATION.value, BrainFlowClassifiers.KNN.value)
model = MLModel (params)
start_time = time.time ()
model.prepare ()
predicted = [model.predict (x) >= 0.5 for x in data[0]]
model.release ()
stop_time = time.time ()
print ('Total time %f' % (stop_time - start_time))
print (metrics.classification_report (data[1], predicted))
def main():
BoardShim.enable_board_logger()
DataFilter.enable_data_logger()
MLModel.enable_ml_logger()
params = BrainFlowInputParams()
board = BoardShim(BoardIds.BRAINBIT_BOARD.value, params)
master_board_id = board.get_board_id()
sampling_rate = BoardShim.get_sampling_rate(master_board_id)
board.prepare_session()
board.start_stream(45000, '')
eeg_channels = BoardShim.get_eeg_channels(int(master_board_id))
while True:
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'start sleeping in the main thread')
time.sleep(
5
) # recommended window size for eeg metric calculation is at least 4 seconds, bigger is better
data = board.get_board_data()
bands = DataFilter.get_avg_band_powers(data, eeg_channels,
sampling_rate, True)
feature_vector = np.concatenate((bands[0], bands[1]))
print(feature_vector)
# calc concentration
concentration_params = BrainFlowModelParams(
BrainFlowMetrics.CONCENTRATION.value,
BrainFlowClassifiers.KNN.value)
concentration = MLModel(concentration_params)
concentration.prepare()
print('Concentration: %f' % concentration.predict(feature_vector))
concentration.release()
# calc relaxation
relaxation_params = BrainFlowModelParams(
BrainFlowMetrics.RELAXATION.value,
BrainFlowClassifiers.REGRESSION.value)
relaxation = MLModel(relaxation_params)
relaxation.prepare()
print('Relaxation: %f' % relaxation.predict(feature_vector))
relaxation.release()
board.stop_stream()
board.release_session()
def liveStream():
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
eeg_channels = BoardShim.get_eeg_channels(board_id)
sampling_rate = BoardShim.get_sampling_rate(board_id)
timestamp = BoardShim.get_timestamp_channel(board_id)
board.prepare_session()
board.start_stream()
while True:
#get board data removes data from the buffer
while board.get_board_data_count() < 250:
time.sleep(0.005)
data = board.get_board_data()
#datadf = pd.DataFrame(np.transpose(data)) #creating a dataframe of the eeg data to extract eeg values later
"""for count, channel in enumerate(eeg_channels):
# filters work in-place
#Check Brainflow docs for more filters
if count == 0:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0, 4.0, 4,
FilterTypes.BUTTERWORTH.value, 0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0, 20.0, 4,
FilterTypes.BESSEL.value, 0) # bandpass 11 - 31
if count == 1:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0, 4.0, 4,
FilterTypes.BUTTERWORTH.value, 0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0, 20.0, 4,
FilterTypes.BESSEL.value, 0) # bandpass 11 - 31
if count == 2:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0, 4.0, 4,
FilterTypes.BUTTERWORTH.value, 0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0, 20.0, 4,
FilterTypes.BESSEL.value, 0) # bandpass 11 - 31
if count == 3:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0, 4.0, 4,
FilterTypes.BUTTERWORTH.value, 0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0, 20.0, 4,
FilterTypes.BESSEL.value, 0) # bandpass 11 - 31"""
#Brainflow ML Model
bands = DataFilter.get_avg_band_powers(data, eeg_channels,
sampling_rate, True)
feature_vector = np.concatenate((bands[0], bands[1]))
print(feature_vector)
# calc concentration
concentration_params = BrainFlowModelParams(
BrainFlowMetrics.CONCENTRATION.value,
BrainFlowClassifiers.KNN.value)
concentration = MLModel(concentration_params)
concentration.prepare()
print('Concentration: %f' % concentration.predict(feature_vector))
concentration.release()
# calc relaxation
relaxation_params = BrainFlowModelParams(
BrainFlowMetrics.RELAXATION.value,
BrainFlowClassifiers.REGRESSION.value)
relaxation = MLModel(relaxation_params)
relaxation.prepare()
print('Relaxation: %f' % relaxation.predict(feature_vector))
relaxation.release()
DataFilter.write_file(data, 'data.csv', 'w') #writing data to csv file
board.stop_stream()
board.release_session()
def main(i):
BoardShim.enable_dev_board_logger()
BoardShim.disable_board_logger(
) #optional. take this out for initial setup for your board.
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
eeg_channels = BoardShim.get_eeg_channels(board_id)
sampling_rate = BoardShim.get_sampling_rate(board_id)
timestamp = BoardShim.get_timestamp_channel(board_id)
board.prepare_session()
board.start_stream()
style.use('fivethirtyeight')
plt.title("Live EEG stream from Brainflow", fontsize=15)
plt.ylabel("Data in millivolts", fontsize=15)
plt.xlabel("\nTime", fontsize=10)
keep_alive = True
eeg1 = [] #lists to store eeg data
eeg2 = []
eeg3 = []
eeg4 = []
timex = [] #list to store timestamp
while keep_alive == True:
while board.get_board_data_count(
) < 250: #ensures that all data shape is the same
time.sleep(0.005)
data = board.get_current_board_data(250)
# creating a dataframe of the eeg data to extract eeg values later
eegdf = pd.DataFrame(np.transpose(data[eeg_channels]))
eegdf_col_names = [
"ch1", "ch2", "ch3", "ch4", "ch5", "ch6", "ch7", "ch8", "ch9",
"ch10", "ch11", "ch12", "ch13", "ch14", "ch15", "ch16"
]
eegdf.columns = eegdf_col_names
# to keep it simple, making another dataframe for the timestamps to access later
timedf = pd.DataFrame(np.transpose(data[timestamp]))
print(
"EEG Dataframe"
) #easy way to check what data is being streamed and if program is working
print(eegdf) #isn't neccesary.
for count, channel in enumerate(eeg_channels):
# filters work in-place
# Check Brainflow docs for more filters
if count == 0:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0,
4.0, 4,
FilterTypes.BUTTERWORTH.value,
0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0,
20.0, 4, FilterTypes.BESSEL.value,
0) # bandpass 11 - 31
if count == 1:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0,
4.0, 4,
FilterTypes.BUTTERWORTH.value,
0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0,
20.0, 4, FilterTypes.BESSEL.value,
0) # bandpass 11 - 31
if count == 2:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0,
4.0, 4,
FilterTypes.BUTTERWORTH.value,
0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0,
20.0, 4, FilterTypes.BESSEL.value,
0) # bandpass 11 - 31
if count == 3:
DataFilter.perform_bandstop(data[channel], sampling_rate, 60.0,
4.0, 4,
FilterTypes.BUTTERWORTH.value,
0) # bandstop 58 - 62
DataFilter.perform_bandpass(data[channel], sampling_rate, 21.0,
20.0, 4, FilterTypes.BESSEL.value,
0) # bandpass 11 - 31
# Brainflow ML Model
bands = DataFilter.get_avg_band_powers(data, eeg_channels,
sampling_rate, True)
feature_vector = np.concatenate((bands[0], bands[1]))
# calc concentration
concentration_params = BrainFlowModelParams(
BrainFlowMetrics.CONCENTRATION.value,
BrainFlowClassifiers.KNN.value)
concentration = MLModel(concentration_params)
concentration.prepare()
print('Concentration: %f' % concentration.predict(feature_vector))
concentrated_measure = concentration.predict(feature_vector)
concentration.release()
# calc relaxation
relaxation_params = BrainFlowModelParams(
BrainFlowMetrics.RELAXATION.value, BrainFlowClassifiers.KNN.value)
relaxation = MLModel(relaxation_params)
relaxation.prepare()
print('Relaxation: %f' % relaxation.predict(feature_vector))
relaxed_measure = relaxation.predict(feature_vector)
relaxation.release()
#appending eeg data to lists
eeg1.extend(
eegdf.iloc[:, 0].values
) # I am using OpenBCI Ganglion board, so I only have four channels.
eeg2.extend(
eegdf.iloc[:, 1].values
) # If you have a different board, you should be able to copy paste
eeg3.extend(eegdf.iloc[:,
2].values) # these commands for more channels.
eeg4.extend(eegdf.iloc[:, 3].values)
timex.extend(timedf.iloc[:, 0].values) # timestamps
plt.cla()
#plotting eeg data
plt.plot(timex, eeg1, label="Channel 1", color="red")
plt.plot(timex, eeg2, label="Channel 2", color="blue")
plt.plot(timex, eeg3, label="Channel 3", color="orange")
plt.plot(timex, eeg4, label="Channel 4", color="purple")
plt.tight_layout()
keep_alive = False #resetting stream so that matplotlib can plot data
if concentrated_measure >= 0.5:
print(
"GOOD KEEP CONCENTRATING"
) #a program screaming at you to concentrate should do the trick :)
else:
print("WHERE IS THE CONCENTRATION??")
if relaxed_measure >= 0.5:
print("YES RELAX MORE")
else:
print("NO, START RELAXING")
board.stop_stream()
board.release_session()