parser.add_argument('-k',
'--api_key',
type=str,
required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
# get the LSL inlet
print(f'looking for a stream with name {args.stream_name}...')
streams = pylsl.resolve_stream('name', args.stream_name)
if len(streams) == 0:
raise RuntimeError(f'Found no LSL streams with name {args.stream_name}')
inlet = pylsl.StreamInlet(streams[0])
# make API calls in a loop
while True:
# construct the call based on the received sample chunk
# lsl chunks group 4-channel samples in a 2D array as follows:
# samples: [[ch1, ch2, ch3, ch4], [ch1, ch2, ch3, ch4], ...]
# the timestamp array corresponds to each received 4-channel grouping:
# timestamps: [ts1, ts2, ...]
chunk, timestamps = inlet.pull_chunk(timeout=5.0, max_samples=256)
eeg_data = [[samples[channel] for samples in chunk]
for channel in range(4)]
calculations = api.request_metrics(
api_key=args.api_key,
eeg_data=eeg_data,
metrics=['eye', 'blink', 'bandpower', 'artifact_detect'],
)
pprint.pprint(calculations)
'''
This script contains an example of checking channels for artifacts in a Petal
Metrics API call. You will need a valid developer API key to access.
Usage: python api_artifacts.py -k $API_KEY
'''
import argparse
import pprint
import random
import api
parser = argparse.ArgumentParser()
parser.add_argument('-k',
'--api_key',
type=str,
required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
random_eeg_data = [[random.randint(0, 100) / 100 for i in range(256)]
for num in range(4)]
calculations = api.request_metrics(
api_key=args.api_key,
eeg_data=random_eeg_data,
metrics=['artifact_count'],
)
pprint.pprint(calculations['artifact_count'])
'''
This script contains an example of a bandpower Petal Metrics API call. You
will need a valid developer API key to access.
Usage: python api_bandpower.py -k $API_KEY
'''
import argparse
import pprint
import random
import api
parser = argparse.ArgumentParser()
parser.add_argument('-k',
'--api_key',
type=str,
required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
random_eeg_data = [[random.randint(0, 100) / 100 for i in range(256)]
for num in range(4)]
calculations = api.request_metrics(
api_key=args.api_key,
eeg_data=random_eeg_data,
metrics=['bandpower'],
)
pprint.pprint(calculations['bandpower'])
import api
parser = argparse.ArgumentParser()
parser.add_argument('-k', '--api_key', type=str, required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
random_eeg_data = [
[random.randint(0,100) / 100 for i in range(150)]
for num in range(4)
]
calculations = api.request_metrics(
api_key=args.api_key,
eeg_data=random_eeg_data,
metrics=['preprocessed_data', 'artifact_count', 'bandpower'],
)
# parse api result and get bandpower
bandpower = calculations["bandpower"]
alphaAverage = 0
for channel in bandpower:
print(f'channel {channel} alpha: {bandpower[channel]["alpha"]}')
alphaAverage += bandpower[channel]['alpha']
# calculate average alpha and print
alphaAverage = alphaAverage/4
print(f'alpha average: {alphaAverage}')
'''
This script contains an example of a preprocessed data Petal Metrics API call.
You will need a valid developer API key to access.
Usage: python api_preprocess.py -k $API_KEY
'''
import argparse
import random
import api
parser = argparse.ArgumentParser()
parser.add_argument('-k',
'--api_key',
type=str,
required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
random_eeg_data = [[random.randint(0, 100) / 100 for i in range(150)]
for num in range(4)]
calculations = api.request_metrics(
api_key=args.api_key,
eeg_data=random_eeg_data,
metrics=['preprocessed_data'],
)
print(calculations['data'])