class CytonBoard(object):
def __init__(self, file):
self.params = BrainFlowInputParams()
#self.params.serial_port = serial_port
self.params.file = file
self.params.other_info = str(BoardIds.CYTON_BOARD.value)
self.board = BoardShim(BoardIds.PLAYBACK_FILE_BOARD.value, self.params)
def start_stream(self):
self.board.prepare_session()
#self.board.config_board ('loopback_true')
self.board.config_board('old_timestamps')
self.board.start_stream()
def stop_stream(self):
self.board.stop_stream()
self.board.release_session()
def poll(self, sample_num):
try:
while self.board.get_board_data_count() < sample_num:
time.sleep(0.02)
except Exception as e:
raise (e)
#print(self.board.get_board_data_count())
board_data = self.board.get_board_data()
#df = board_2_df(np.transpose(board_data))
return board_data
def sampling_frequency(self):
sampling_freq = self.board.get_sampling_rate(
BoardIds.CYTON_BOARD.value)
return sampling_freq
class CytonBoard(object):
def __init__(self, serial_port):
self.params = BrainFlowInputParams()
self.params.serial_port = serial_port
self.board = BoardShim(BoardIds.CYTON_BOARD.value, self.params)
def start_stream(self):
self.board.prepare_session()
self.board.start_stream()
def stop_stream(self):
self.board.stop_stream()
self.board.release_session()
def poll(self, sample_num):
try:
while self.board.get_board_data_count() < sample_num:
time.sleep(0.02)
except Exception as e:
raise (e)
board_data = self.board.get_board_data()
#df = board_2_df(np.transpose(board_data))
return board_data
def sampling_frequency(self):
sampling_freq = self.board.get_sampling_rate(
BoardIds.CYTON_BOARD.value)
return sampling_freq
class CytonBoard(object):
def __init__(self, serial_port):
self.params = BrainFlowInputParams()
self.params.serial_port = serial_port
self.board = BoardShim(BoardIds.CYTON_BOARD.value, self.params)
def start_stream(self):
self.board.prepare_session()
self.board.start_stream()
def stop_stream(self):
self.board.stop_stream()
self.board.release_session()
def poll(self, sample_num):
try:
while self.board.get_board_data_count() < sample_num:
time.sleep(0.02)
except Exception as e:
raise (e)
board_data = self.board.get_board_data()
DataFilter.write_file(board_data, '.\Data\cyton_data_new.txt',
'a') # 'a' appends; 'w' overwrites
# Could add check to see if file already exists, adding a 1, 2, etc. on the end to avoid conflict
# Could use date function for generating names based on date-time.
df = board_2_df(np.transpose(board_data))
#print('/n')
#print(df)
return df
def sampling_frequency(self):
sampling_freq = self.board.get_sampling_rate(
BoardIds.CYTON_BOARD.value)
return sampling_freq
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()
