def initialize_board(name='SYNTHETIC',port = None):
if name == 'SYNTHETIC':
BoardShim.enable_dev_board_logger()
# use synthetic board for demo
params = BrainFlowInputParams()
board_id = BoardIds.SYNTHETIC_BOARD.value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.accel_channels = BoardShim.get_accel_channels(board_id)
elif name == 'OPENBCI':
board_id = BoardIds.CYTON_DAISY_BOARD.value
params = BrainFlowInputParams()
params.serial_port = port
board_id = BoardIds.CYTON_DAISY_BOARD.value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.accel_channels = BoardShim.get_accel_channels(board_id)
print('Must have OpenBCI GUI open to work... (as port is not opened by Brainflow)')
board.prepare_session()
return board
def __init__(self, data_idx=101, board=None):
self.data_idx = data_idx
self.board_id = 2
self.eeg_channels = BoardShim.get_eeg_channels(self.board_id)
self.ts_channels = BoardShim.get_timestamp_channel(self.board_id)
sfreq = BoardShim.get_sampling_rate(self.board_id)
self.freq = sfreq
ch_types = [
'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg',
'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg'
]
ch_names = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
'N', 'O', 'P'
]
self.info = mne.create_info(ch_names=ch_names,
sfreq=sfreq,
ch_types=ch_types)
if board is not None:
self.board = board
else:
params = BrainFlowInputParams()
params.serial_port = "/dev/cu.usbserial-DM01MTXZ"
board = BoardShim(self.board_id, params)
self.board.prepare_session()
self.board.start_stream()
def start_recording(total_sec, max_samples, params, args):
board = BoardShim(args.board_id, params)
board.prepare_session()
# board.start_stream () # use this for default options
board.start_stream(max_samples, args.streamer_params)
time.sleep(total_sec)
# 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
timestamp_channel = board.get_timestamp_channel(board_id=0)
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)
print(eeg_channels)
df = pd.DataFrame(np.transpose(data))
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('data/before_processing.png')
# for demo apply different filters to different channels, in production choose one
for count, channel in enumerate(eeg_channels):
DataFilter.perform_lowpass(data[channel],
BoardShim.get_sampling_rate(args.board_id),
30, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_highpass(data[channel],
BoardShim.get_sampling_rate(args.board_id),
5, 3, FilterTypes.BUTTERWORTH.value, 0)
DataFilter.perform_bandstop(data[channel],
BoardShim.get_sampling_rate(args.board_id),
50, 2, 8, FilterTypes.BUTTERWORTH.value, 0)
df = pd.DataFrame(np.transpose(
data[:, 1000:])) # Usable after 1000 given order of filters are 3
plt.figure()
df[eeg_channels].plot(subplots=True)
plt.savefig('data/after_processing.png')
return data, timestamp_channel
def save_data(self):
data = self.board.get_current_board_data(
self.board.get_board_data_count())
ts_channels = BoardShim.get_timestamp_channel(self.board_id)
eeg_data = data[self.eeg_channels, :]
eeg_ts = data[ts_channels, :]
eeg_data = eeg_data / 1000000
np.save("raw_data.npy", eeg_data)
raw = mne.io.RawArray(eeg_data, self.info)
raw2 = mne.io.RawArray(np.copy(eeg_data),
self.info) # do this if need be ...
raw.filter(0.5, 40., fir_design='firwin')
raw2.filter(9., 50., fir_design='firwin')
data1 = raw.get_data()
data2 = raw2.get_data()
np.save("./data/eeg_data_{0}_old.npy".format(self.data_idx),
np.transpose(data1))
np.save("./data/eeg_fft_data_{0}_old.npy".format(self.data_idx),
np.transpose(data2))
np.save("./data/eeg_timestamps_{0}_old.npy".format(self.data_idx),
eeg_ts)
def main(i):
board_id = BoardIds.SYNTHETIC_BOARD.value
eeg_channels = BoardShim.get_eeg_channels(board_id)
timestamp = BoardShim.get_timestamp_channel(board_id)
style.use('fivethirtyeight')
plt.title("Live EEG Datastream from Brainflow", fontsize=15)
plt.ylabel("Data in millivolts", fontsize=15)
plt.xlabel("\nTime", fontsize=10)
data = DataFilter.read_file('data.csv')
eegdf = pd.DataFrame(np.transpose(data[eeg_channels, timestamp]))
#timedf = pd.DataFrame(np.transpose(data[timestamp])) #to keep it simple, making another dataframe for the timestamps to access later
eegdf_col_names = ["ch1","ch2","ch3","ch4","ch5","ch6","ch7","ch8","ch9","ch10","ch11","ch12","ch13","ch14","ch15","ch16"]
eegdf.columns = eegdf_col_names
print("EEG Dataframe")
print(eegdf) #easy way to check what data is being streamed and if program is working
#print(eegdf)
eeg1 = eegdf.iloc[:, 0].values #I am using OpenBCI Ganglion board, so only have four channels.
eeg2 = eegdf.iloc[:, 1].values
eeg3 = eegdf.iloc[:, 2].values
eeg4 = eegdf.iloc[:, 3].values
timex= eegdf.iloc[:, 15].values #timestamps
#print(timex) #use this to see what the UNIX timestamps look like
print("EEG Channel 1")
print(eeg1)
#print("Time DF")
#print(timedf)
print("Timestamp")
print(timex)
"""plt.cla()
def main():
'''
Connect to the Brainstorm
'''
# Authentication
username = '******'
password = ''
## Set Connection Details
# brainstorm = brainsatplay.Brainstorm('https://localhost') # Local
brainstorm = brainsatplay.Brainstorm() # Deployed Server
## Connect
res = brainstorm.connect(username,
password) # All optional (defaults to guest)
'''
Subscribe to a Particular Game
'''
# # Connection Settings
# appname = 'brainstorm'
# devices = []
# props = ['raw','times','sps','deviceType','format','eegChannelTags']
# sessionid = None
# spectating = False # Spectate to view data without sending it
# res = brainstorm.getSessions(appname)
# if res['msg'] != 'appNotFound':
# sessionid = res['sessions'][0]['id']
# else:
# res = brainstorm.createSession(appname, devices, props)
# sessionid = res['sessionInfo']['id']
# # Handle Data from Subscribed Games
# def newData(json):
# for user in json['userData']:
# name = user['username']
# print('Data for {}'.format(name))
# res = brainstorm.subscribeToSession(sessionid,spectating, newData)
'''
Stream your Data
'''
# Setup Brainflow
params = BrainFlowInputParams()
board_id = BoardIds['SYNTHETIC_BOARD'].value
board = BoardShim(board_id, params)
board.rate = BoardShim.get_sampling_rate(board_id)
board.channels = BoardShim.get_eeg_channels(board_id)
board.time_channel = BoardShim.get_timestamp_channel(board_id)
board.eeg_channels = BoardShim.get_eeg_channels(board_id)
board.eeg_names = BoardShim.get_eeg_names(board_id)
board.prepare_session()
board.start_stream(num_samples=450000)
# Handle CTRL-C Exit
def onStop():
board.stop_stream()
board.release_session()
loopCount = 0
# Start Stream Loop
def streamLoop():
pass_data = []
rate = DataFilter.get_nearest_power_of_two(board.rate)
data = board.get_board_data()
t = data[board.time_channel]
data = data[board.eeg_channels]
for entry in data:
pass_data.append((entry).tolist())
data = {}
data['raw'] = pass_data
data['times'] = t.tolist()
# Send Metadata on First Loop
if loopCount == 0:
data['sps'] = board.rate
data['deviceType'] = 'eeg'
data['format'] = 'brainflow'
tags = []
for i, channel in enumerate(board.eeg_channels):
tags.append({
'ch': channel - 1,
'tag': board.eeg_names[i],
'analyze': True
})
data['eegChannelTags'] = tags
return data
res = brainstorm.startStream(streamLoop, onStop)
def main():
parser = argparse.ArgumentParser()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
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='other info',
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('--log', action='store_true')
parser.add_argument('--run-time',
type=int,
help='run time for one iteration in sec',
required=True)
parser.add_argument('--num-iters',
type=int,
help='number of iterations',
default=1)
parser.add_argument(
'--channels',
type=str,
help='channels to plot in format 0,1,2 by default plot all channels',
default=None)
parser.add_argument('--config-file',
type=str,
help='file with strings to send to device',
default=None)
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.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
if (args.log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
# for streaming board need to use master board id
master_board_id = args.board_id
if args.board_id == BoardIds.STREAMING_BOARD.value:
master_board_id = int(params.other_info)
board = BoardShim(args.board_id, params)
board.prepare_session()
if args.config_file:
with open(args.config_file) as file:
lines = file.readlines()
for line in lines:
board.config_board(line)
buffer_size = int(
BoardShim.get_sampling_rate(master_board_id) * args.run_time *
1.2) # + 20% for safety
if master_board_id in (BoardIds.CYTON_BOARD.value,
BoardIds.CYTON_WIFI_BOARD.value,
BoardIds.GANGLION_WIFI_BOARD.value):
bytes_per_package = 33
elif master_board_id in (BoardIds.CYTON_DAISY_BOARD,
BoardIds.CYTON_DAISY_WIFI_BOARD.value):
bytes_per_package = 66
elif master_board_id == BoardIds.SYNTHETIC_BOARD.value:
bytes_per_package = 104
elif master_board_id == BoardIds.NOVAXR_BOARD.value:
bytes_per_package = 72
else:
raise ValueError('unsupported board')
timestamp_channel = BoardShim.get_timestamp_channel(master_board_id)
package_num_channel = BoardShim.get_package_num_channel(master_board_id)
try:
cur_id = 0
for i in range(args.num_iters):
# wait for an input
input('Press Enter to continue...')
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'\nRunning iteration %d/%d\n' % (i, args.num_iters))
# start stream and get data
board.start_stream(buffer_size, args.streamer_params)
time.sleep(args.run_time)
board.stop_stream()
data = board.get_board_data()
if data.shape[1] == 0:
BoardShim.log_message(LogLevels.LEVEL_WARN.value,
'\nNo data received!\n')
continue
# calculate some metrics
total_bytes_received = bytes_per_package * data.shape[1]
packages_per_sec = float(data.shape[1]) / float(args.run_time)
timestamp_array = data[timestamp_channel]
time_diff_array = list()
for i in range(0, timestamp_array.size - 1):
time_diff_array.append(timestamp_array[i + 1] -
timestamp_array[i])
package_num_array = data[package_num_channel]
lost_packages = 0
expected = package_num_array[0]
while cur_id < package_num_array.size:
if expected == 256:
expected = 0
if package_num_array[cur_id] != expected:
BoardShim.log_message(
LogLevels.LEVEL_WARN.value,
'package loss detected: position %d package_num value %d expected value %d'
% (cur_id, package_num_array[cur_id], expected))
lost_packages = lost_packages + 1
else:
cur_id = cur_id + 1
expected = expected + 1
package_loss = (lost_packages / data.shape[1]) * 100
# provide results for iteration
BoardShim.log_message(LogLevels.LEVEL_INFO.value, '\nResults:\n')
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'package loss percent %f' % package_loss)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'average time delta %f' % statistics.mean(time_diff_array))
BoardShim.log_message(
LogLevels.LEVEL_INFO.value, 'std deviation of time delta %f' %
statistics.pstdev(time_diff_array))
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total packages received %d' % data.shape[1])
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'packages per sec %f' % packages_per_sec)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'total bytes received %d' % total_bytes_received)
# plot data
eeg_channels = BoardShim.get_eeg_channels(master_board_id)
emg_channels = BoardShim.get_emg_channels(master_board_id)
total_channels = list()
if args.channels is not None:
selected_channels = [int(x) for x in args.channels.split(',')]
temp_channels = eeg_channels
for ch in emg_channels:
if ch not in temp_channels:
temp_channels.append(ch)
for i in range(len(temp_channels)):
if i in selected_channels:
total_channels.append(temp_channels[i])
else:
# for cyton/ganglion eeg_channels and emg_channels are the same array because we can not split it
# for novaxr its 2 different arrays, join them
total_channels = eeg_channels
for ch in emg_channels:
if ch not in total_channels:
total_channels.append(ch)
total_channels.append(timestamp_channel)
columns = list()
for i in range(len(total_channels) - 1):
columns.append('channel_%d' % (int(total_channels[i]) - 1))
columns.append('timestamp')
df = pd.DataFrame(np.transpose(data))
df.to_csv('all_data_%d.csv' % i)
df_to_plot = df[total_channels]
df_to_plot.columns = columns
df_to_plot.to_csv('selected_data_%d.csv' % i)
df_to_plot.plot(subplots=True, x='timestamp', style='.-')
plt.show()
finally:
# release session in the end
board.release_session()
class OpenBCIInterface:
def __init__(self,
serial_port='COM5',
board_id=0,
log='store_true',
streamer_params='',
ring_buffer_size=45000): # default board_id 2 for Cyton
params = BrainFlowInputParams()
params.serial_port = serial_port
params.ip_port = 0
params.mac_address = ''
params.other_info = ''
params.serial_number = ''
params.ip_address = ''
params.ip_protocol = 0
params.timeout = 0
params.file = ''
self.streamer_params = streamer_params
self.ring_buffer_size = ring_buffer_size
self.board_id = board_id
self.info_eeg = None
self.info_aux = None
self.outlet_eeg = None
self.outlet_aux = None
if (log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
self.board = BoardShim(board_id, params)
def start_sensor(self):
# tell the sensor to start sending frames
self.board.prepare_session()
print('OpenBCIInterface: connected to sensor')
print(self.board.get_board_id())
try:
self.board.start_stream(self.ring_buffer_size,
self.streamer_params)
self.infor_test()
except brainflow.board_shim.BrainFlowError:
print('OpenBCIInterface: Board is not ready.')
def process_frames(self):
# return one or more frames of the sensor
frames = self.board.get_board_data()
return frames
def stop_sensor(self):
try:
self.board.stop_stream()
print('OpenBCIInterface: stopped streaming.')
self.board.release_session()
print('OpenBCIInterface: released session.')
except brainflow.board_shim.BrainFlowError as e:
print(e)
def create_lsl(self,
name='OpenBCI_Cyton_8',
type='EEG',
channel_count=8,
nominal_srate=250.0,
channel_format='float32',
source_id='Cyton_0'):
self.info_eeg = StreamInfo(name=name,
type=type,
channel_count=channel_count,
nominal_srate=nominal_srate,
channel_format=channel_format,
source_id='')
chns = self.info_eeg.desc().append_child('channels')
self.labels = ['Fp1', 'Fp2', 'C3', 'C4', 'T5', 'T6', 'O1', 'O2']
for label in self.labels:
ch = chns.append_child("channel")
ch.append_child_value('label', label)
ch.append_child_value('unit', 'microvolts')
ch.append_child_value('type', 'EEG')
self.info_eeg.desc().append_child_value('manufacturer', 'OpenBCI Inc.')
self.outlet_eeg = StreamOutlet(self.info_eeg)
print("--------------------------------------\n" + \
"LSL Configuration: \n" + \
" Stream 1: \n" + \
" Name: " + name + " \n" + \
" Type: " + type + " \n" + \
" Channel Count: " + str(channel_count) + "\n" + \
" Sampling Rate: " + str(nominal_srate) + "\n" + \
" Channel Format: " + channel_format + " \n" + \
" Source Id: " + source_id + " \n")
def push_sample(self, samples):
self.outlet_eeg.push_sample(samples)
def infor_test(self):
print(self.board.get_eeg_names(self.board_id))
print(self.board.get_sampling_rate(self.board_id))
print(self.board.get_board_id())
print(self.board.get_package_num_channel(self.board_id))
print(self.board.get_timestamp_channel(self.board_id))
print(self.board.get_eeg_channels(self.board_id))
print(self.board.get_accel_channels(self.board_id))
print(self.board.get_marker_channel(self.board_id))
print(self.board.get_other_channels(self.board_id))
print(self.board.get_analog_channels(self.board_id))
print(self.board.get_other_channels(self.board_id))
def main():
parser = argparse.ArgumentParser()
# use docs to check which parameters are required for specific board, e.g. for Cyton - set serial port
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='other info',
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('--log', action='store_true')
parser.add_argument('--run-time',
type=int,
help='run time in sec',
required=True)
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.ip_address = args.ip_address
params.ip_protocol = args.ip_protocol
if (args.log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
# for streaming board need to use master board id
master_board_id = args.board_id
if args.board_id == BoardIds.STREAMING_BOARD.value:
master_board_id = int(params.other_info)
board = BoardShim(args.board_id, params)
board.prepare_session()
buffer_size = int(
BoardShim.get_sampling_rate(master_board_id) * args.run_time *
1.2) # + 20% for safety
board.start_stream(buffer_size, args.streamer_params)
time.sleep(args.run_time)
board.stop_stream()
data = board.get_board_data()
board.release_session()
if master_board_id in (BoardIds.CYTON_BOARD.value,
BoardIds.CYTON_WIFI_BOARD.value,
BoardIds.GANGLION_WIFI_BOARD.value):
bytes_per_package = 33
elif master_board_id in (BoardIds.CYTON_DAISY_BOARD,
BoardIds.CYTON_DAISY_WIFI_BOARD.value):
bytes_per_package = 66
elif master_board_id == BoardIds.SYNTHETIC_BOARD.value:
bytes_per_package = 104
elif master_board_id == BoardIds.NOVAXR_BOARD.value:
bytes_per_package = 72
else:
raise ValueError('unsupported board')
total_bytes_received = bytes_per_package * data.shape[1]
packages_per_sec = float(data.shape[1]) / float(args.run_time)
timestamp_channel = BoardShim.get_timestamp_channel(master_board_id)
timestamp_array = data[timestamp_channel]
time_diff_array = list()
for i in range(0, timestamp_array.size - 1):
time_diff_array.append(timestamp_array[i + 1] - timestamp_array[i])
package_num_channel = BoardShim.get_package_num_channel(master_board_id)
package_num_array = data[package_num_channel]
lost_packages = 0
expected = 0
cur_id = 0
while cur_id < package_num_array.size:
if expected == 256:
expected = 0
if package_num_array[cur_id] != expected:
BoardShim.log_message(
LogLevels.LEVEL_WARN.value,
'package loss detected: position %d package_num value %d expected value %d'
% (cur_id, package_num_array[cur_id], expected))
lost_packages = lost_packages + 1
else:
cur_id = cur_id + 1
expected = expected + 1
package_loss = (lost_packages / data.shape[1]) * 100
BoardShim.log_message(LogLevels.LEVEL_INFO.value, '\nResults:\n')
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'package loss percent %f' % package_loss)
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'average time delta %f' % statistics.mean(time_diff_array))
BoardShim.log_message(
LogLevels.LEVEL_INFO.value,
'std deviation of time delta %f' % statistics.pstdev(time_diff_array))
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total packages received %d' % data.shape[1])
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'packages per sec %f' % packages_per_sec)
BoardShim.log_message(LogLevels.LEVEL_INFO.value,
'total bytes received %d' % total_bytes_received)
eeg_channels = BoardShim.get_eeg_channels(master_board_id)
emg_channels = BoardShim.get_emg_channels(master_board_id)
total_channels = eeg_channels
# for cyton/ganglion eeg_channels and emg_channels are the same array because we can not split it
# for novaxr its 2 different arrays, join them
for ch in emg_channels:
if ch not in total_channels:
total_channels.append(ch)
df = pd.DataFrame(np.transpose(data))
df[total_channels].to_csv('eeg_emg_data.csv')
df.to_csv('all_data.csv')
plt.figure()
df[total_channels].plot(subplots=True)
plt.show()
class OpenBCILSLInterface:
def __init__(self, stream_name='OpenBCI_Cyton_8', stream_type='EEG', serial_port='COM5', board_id="0",
log='store_true', streamer_params='',
ring_buffer_size=45000): # default board_id 2 for Cyton
self.params = BrainFlowInputParams()
self.params.serial_port = serial_port
self.params.ip_port = 0
self.params.mac_address = ''
self.params.other_info = ''
self.params.serial_number = ''
self.params.ip_address = ''
self.params.ip_protocol = 0
self.params.timeout = 0
self.params.file = ''
self.stream_name = stream_name
self.stream_type = stream_type
self.board_id = int(board_id)
self.streamer_params = streamer_params
self.ring_buffer_size = ring_buffer_size
if (log):
BoardShim.enable_dev_board_logger()
else:
BoardShim.disable_board_logger()
try:
self.board = BoardShim(self.board_id, self.params)
self.info_print()
except brainflow.board_shim.BrainFlowError:
print('Cannot connect to board')
def start_sensor(self):
# tell the sensor to start sending frames
try:
self.board.prepare_session()
except brainflow.board_shim.BrainFlowError:
raise AssertionError('Unable to connect to device: please check the sensor connection or the COM port number in device preset.')
print('OpenBCIInterface: connected to sensor')
try:
self.board.start_stream(self.ring_buffer_size, self.streamer_params)
except brainflow.board_shim.BrainFlowError:
raise AssertionError('Unable to connect to device: please check the sensor connection or the COM port number in device preset.')
print('OpenBCIInterface: connected to sensor')
self.create_lsl(name=self.stream_name,
type=self.stream_type,
nominal_srate=self.board.get_sampling_rate(self.board_id),
channel_format='float32',
source_id='Cyton_' + str(self.board_id))
def process_frames(self):
# return one or more frames of the sensor
frames = self.board.get_board_data()
for frame in frames.T:
self.push_frame(frame)
# TODO: fine push chunk error
# frames = np.transpose(frames)
# tesst = [[rand() for chan_ix in range(24)]
# for samp_ix in range(6)]
# # frames = frames.astype('float32')
# if frames.shape[0] > 0:
# self.push_frame(samples=frames)
return frames
def stop_sensor(self):
try:
self.board.stop_stream()
print('OpenBCIInterface: stopped streaming.')
self.board.release_session()
print('OpenBCIInterface: released session.')
except brainflow.board_shim.BrainFlowError as e:
print(e)
def create_lsl(self, name='OpenBCI_Cyton_8', type='EEG',
nominal_srate=250.0, channel_format='float32',
source_id='Cyton_0'):
channel_count = self.board.get_num_rows(self.board_id)
self.info_eeg = StreamInfo(name=name, type=type, channel_count=channel_count,
nominal_srate=nominal_srate, channel_format=channel_format,
source_id=source_id)
# chns = self.info_eeg.desc().append_child('channels')
#
# self.labels = ['Fp1', 'Fp2', 'C3', 'C4', 'T5', 'T6', 'O1', 'O2']
#
# for label in self.labels:
# ch = chns.append_child("channel")
# ch.append_child_value('label', label)
# ch.append_child_value('unit', 'microvolts')
# ch.append_child_value('type', 'EEG')
#
# self.info_eeg.desc().append_child_value('manufacturer', 'OpenBCI Inc.')
self.outlet_eeg = StreamOutlet(self.info_eeg)
print("--------------------------------------\n" + \
"LSL Configuration: \n" + \
" Stream 1: \n" + \
" Name: " + name + " \n" + \
" Type: " + type + " \n" + \
" Channel Count: " + str(channel_count) + "\n" + \
" Sampling Rate: " + str(nominal_srate) + "\n" + \
" Channel Format: " + channel_format + " \n" + \
" Source Id: " + source_id + " \n")
def push_frame(self, samples):
self.outlet_eeg.push_sample(samples)
def info_print(self):
print("Board Information:")
print("Sampling Rate:", self.board.get_sampling_rate(self.board_id))
print("Board Id:", self.board.get_board_id())
print("EEG names:", self.board.get_eeg_names(self.board_id))
print("Package Num Channel: ", self.board.get_package_num_channel(self.board_id))
print("EEG Channels:", self.board.get_eeg_channels(self.board_id))
print("Accel Channels: ", self.board.get_accel_channels(self.board_id))
print("Other Channels:", self.board.get_other_channels(self.board_id))
print("Analog Channels: ", self.board.get_analog_channels(self.board_id))
print("TimeStamp: ", self.board.get_timestamp_channel(self.board_id))
print("Marker Channel: ", self.board.get_marker_channel(self.board_id))
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()
# assuming that there is one directory per session (i.e. morning and night)
todays_sessions = os.listdir(todays_sessions_path)
curr_sesh_name = f"Session_{len(todays_sessions)+1}"
if args.synth:
curr_sesh_name += "_SYNTH"
current_session_dir = os.path.join(todays_sessions_path, curr_sesh_name)
os.mkdir(current_session_dir)
raw_data_path = os.path.join(current_session_dir, "raw_data.csv")
board_id = BoardIds.SYNTHETIC_BOARD.value if args.synth else BoardIds.CYTON_DAISY_BOARD.value
sampling_rate = BoardShim.get_sampling_rate(board_id)
eegs = BoardShim.get_eeg_channels(board_id)
battery = BoardShim.get_battery_channel(board_id)
resistance = BoardShim.get_resistance_channels(board_id)
timestamp = BoardShim.get_timestamp_channel(board_id)
others = BoardShim.get_accel_channels(
board_id) if args.synth else BoardShim.get_other_channels(board_id)
columns = ['timestamp'] + [f'ch_{x+1}' for x in range(len(eegs))
] + [f'aux_{x+1}' for x in range(len(others))]
with open(raw_data_path, 'w') as raw:
raw.write(','.join(columns) + '\n')
if args.debug:
BoardShim.enable_dev_board_logger()
for c in ['eegs', 'battery', 'resistance', 'others', 'timestamp']:
log_debug(f'Channel for {c} is {eval(c)} ')
log_debug(f"sampling rate is {sampling_rate} with board ID {board_id}")
DataFilter.write_file
BUFFER_TIME = args.buffer
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()
