self.id = packet_id
self.channel_data = channel_data
self.aux_data = aux_data
###############################################################################
# Get the data to be simulated from file
'''
If it were real-time the data would come directly from eeg.
For the simulation purpose it has to be read from the file.
'''
# Specify file with eeg data.
eeg_file = '../example_data/blink_00.csv'
# Read requested data.
data = read_csv(eeg_file)
###############################################################################
# Main function of the program #
###############################################################################
# [Child process] Objects creation and main loop
# Define a function to run as the background process (foreground will be the
# graphical interface.
def func_ssvep_det(ssvep_state):
def handle_sample(sample):
# get data point (value) form the first channel (index '0')
smp = sample.channel_data[channel]
self.id = packet_id
self.channel_data = channel_data
self.aux_data = aux_data
###############################################################################
# Get the data to be simulated from file
'''
If it were real-time the data would come directly from eeg.
For the simulation purpose it has to be read from the file.
'''
# Specify file with eeg data.
eeg_file = '../example_data/blink_00.csv'
# Read requested data.
data = read_csv(eeg_file)
###############################################################################
# Main function of the program #
###############################################################################
# [Child process] Objects creation and main loop
# Define a function to run as the background process (foreground will be the
# graphical interface.
def func_ssvep_det(ssvep_state):
def handle_sample(sample):
# get data point (value) form the first channel (index '0')
smp = sample.channel_data[channel]
def plot_time(eeg_file,
fs,
channel,
bandstop=(49, 51),
bandpass=(1, 50),
order=4,
sec_remove=None,
show=True,
xticklabels='samples',
title='Signal time domain after filtration',
sec_range=None,
ylim=None,
threshold=None,
linewidth=1.0):
'''
This function has dual purpose: plotting eeg signal or visualising
threshold detection line.
sec_remove - remove first n seconde (e.g. for filter settling time)
'''
# in case someone passes float value (it has to be int for indexing)
fs = int(fs)
data = read_csv(eeg_file, channel=channel)
if bandstop or bandpass:
# filter data using bandstop (first), and then bandpass filter
filtered_data = filter_eeg(data,
fs,
bandstop=bandstop,
bandpass=bandpass,
order=order)
if sec_remove:
filtered_data = filtered_data[int(fs * sec_remove):]
elif sec_range:
filtered_data = \
filtered_data[int(fs*sec_range[0]):int(fs*sec_range[1])]
data_plotted = filtered_data
else:
data_plotted = data
if xticklabels == 'seconds':
xlabel = 'Time [s]'
if sec_range:
x_axis = np.arange(0,
len(data_plotted) / float(fs),
1 / float(fs)) + sec_range[0]
else:
x_axis = np.arange(0, len(data_plotted) / float(fs), 1 / float(fs))
else:
xlabel = 'Sample number'
x_axis = np.arange(0, len(data_plotted))
fig, ax = plt.subplots()
plt.plot(x_axis, data_plotted, linewidth=1.5, label='signal')
ax.set_xlabel(xlabel, labelpad=15)
ax.set_ylabel(r'microvolts [$\mu$V]', labelpad=20)
if threshold:
ax.text(x_axis[0] - 0.12,
45,
r'50',
style='oblique',
fontsize=30,
color='red')
# draw threshold line
plt.plot((x_axis[0], x_axis[-1]), (threshold, threshold),
'r-',
linewidth=linewidth,
label='threshold')
# change default title
plt.title('Threshold Blink Detection', y=1.02)
leg = plt.legend(bbox_to_anchor=(0.985, 0.14))
llines = leg.get_lines()
plt.setp(llines, linewidth=5)
# set y axis boundries
if ylim:
plt.ylim(ylim)
plt.rcParams.update({'font.size': 32})
# show result
if show:
plt.show()
############################################ # # # GET DATA FROM FILE # # # ############################################ # location of the file with eeg data eeg_file = '/home/jesmasta/eeg_data/OpenBCI/ssvep_00.csv' # get data form channel X channel = 0 # read requested data form csv file data = read_csv(eeg_file, channel) ############################################ # # # FILTER DATA # # # ############################################ # filter data using first, bandstop filter, then bandpass filter filtered_data = filter_eeg(data, fs, bandstop=(49, 51), bandpass=(1, 50)) # get signal for 2nd second - how many you have to cut depends on the order # of the digital filter you apply to the signal filtered_data = filtered_data[int(fs):]
def plot_time(
eeg_file, fs, channel,
bandstop=(49, 51), bandpass=(1, 50), order=4,
sec_remove=None, show=True, xticklabels='samples',
title='Signal time domain after filtration',
sec_range=None, ylim=None, threshold=None, linewidth=1.0
):
'''
This function has dual purpose: plotting eeg signal or visualising
threshold detection line.
sec_remove - remove first n seconde (e.g. for filter settling time)
'''
# in case someone passes float value (it has to be int for indexing)
fs = int(fs)
data = read_csv(eeg_file, channel=channel)
if bandstop or bandpass:
# filter data using bandstop (first), and then bandpass filter
filtered_data = filter_eeg(
data, fs, bandstop=bandstop, bandpass=bandpass,
order=order
)
if sec_remove:
filtered_data = filtered_data[int(fs*sec_remove):]
elif sec_range:
filtered_data = \
filtered_data[int(fs*sec_range[0]):int(fs*sec_range[1])]
data_plotted = filtered_data
else:
data_plotted = data
if xticklabels == 'seconds':
xlabel = 'Time [s]'
if sec_range:
x_axis = np.arange(
0, len(data_plotted)/float(fs), 1/float(fs)
) + sec_range[0]
else:
x_axis = np.arange(0, len(data_plotted)/float(fs), 1/float(fs))
else:
xlabel = 'Sample number'
x_axis = np.arange(0, len(data_plotted))
fig, ax = plt.subplots()
plt.plot(x_axis, data_plotted, linewidth=1.5, label='signal')
ax.set_xlabel(xlabel, labelpad=15)
ax.set_ylabel(r'microvolts [$\mu$V]', labelpad=20)
if threshold:
ax.text(
x_axis[0]-0.12, 45, r'50',
style='oblique', fontsize=30, color='red'
)
# draw threshold line
plt.plot(
(x_axis[0], x_axis[-1]),
(threshold, threshold),
'r-', linewidth=linewidth, label='threshold'
)
# change default title
plt.title('Threshold Blink Detection', y=1.02)
leg = plt.legend(bbox_to_anchor=(0.985, 0.14))
llines = leg.get_lines()
plt.setp(llines, linewidth=5)
# set y axis boundries
if ylim:
plt.ylim(ylim)
plt.rcParams.update({'font.size': 32})
# show result
if show:
plt.show()