def __init__(self,
user_id,
device_type,
rmq_address,
rmq_user,
rmq_pwd,
input_metrics,
output_metrics):
self.module_id = str(uuid.uuid4())
self.user_id = user_id
self.device_type = device_type
self.rmq_address = rmq_address
self.rmq_user = rmq_user
self.rmq_pwd = rmq_pwd
self.input_metrics = None
self.output_metrics = None
self.eeg_subscriber = None
self.mu_publisher = None
self.routing_keys = None
self.preprocessor = None
self.num_channels = get_num_channels(self.device_type, "eeg")
self.eeg_data = np.zeros((0, self.num_channels))
self.count = 0
self.eyeblinks_remover = EyeBlinksFilter()
self.step_size = None
self.electrodes_placement = None
self.enable_ica = False
self.started_fit = False
class PreprocessingModule(object):
def __init__(self,
user_id,
device_type,
rmq_address,
rmq_user,
rmq_pwd,
input_metrics,
output_metrics):
self.module_id = str(uuid.uuid4())
self.user_id = user_id
self.device_type = device_type
self.rmq_address = rmq_address
self.rmq_user = rmq_user
self.rmq_pwd = rmq_pwd
self.input_metrics = None
self.output_metrics = None
self.eeg_subscriber = None
self.mu_publisher = None
self.routing_keys = None
self.preprocessor = None
self.num_channels = get_num_channels(self.device_type, "eeg")
self.eeg_data = np.zeros((0, self.num_channels))
self.count = 0
self.eyeblinks_remover = EyeBlinksFilter()
self.step_size = None
self.electrodes_placement = None
self.enable_ica = False
self.started_fit = False
def configure(self, step_size, electrodes_placement, enable_ica=False):
"""
Module specific params.
@param step_size: (int) STFT step size
@param electrodes_placement: (dict) dict with the electrode placement
for optional Laplacian filtering.
E.g:
{
"channel_2": {
"main": "channel_2",
"artifact": ["channel_0", "channel_3", "channel_5"]
},
"channel_4": {
"main": "channel_4",
"artifact": ["channel_1", "channel_3", "channel_6"]
},
}
If you don't want any Laplacian filtering then set this to:
{
"channel_2": {
"main": "channel_2",
"artifact": []
},
"channel_4": {
"main": "channel_4",
"artifact": []
},
}
More about Laplacian filtering: http://sccn.ucsd.edu/wiki/Flt_laplace
@param input_metric: (string) name of the input metric.
@param output_metric: (string) name of the output metric.
@param enable_ica: (boolean) if 1, enable ICA pre-processing. This will
remove eye blinks.
"""
self.step_size = step_size
self.electrodes_placement = electrodes_placement
self.input_metric = input_metric
self.output_metric = output_metric
self.enable_ica = enable_ica
def connect(self):
"""
Initialize EEG preprocessor, publisher, and subscriber
"""
if self.step_size is None:
raise ValueError("Step size can't be none. "
"Use configure() to set it.")
if self.electrodes_placement is None:
raise ValueError("Electrode placement can't be none. "
"Use configure() to set it.")
if self.input_metric is None:
raise ValueError("Input metric can't be none. "
"Use configure() to set it.")
if self.output_metric is None:
raise ValueError("Output metric can't be none. "
"Use configure() to set it.")
self.routing_keys = {
self.input_metric: _ROUTING_KEY % (self.user_id, self.device_type,
self.input_metric),
self.output_metric: _ROUTING_KEY % (self.user_id, self.device_type,
self.output_metric)
}
self.mu_publisher = PikaPublisher(self.rmq_address,
self.rmq_user, self.rmq_pwd)
self.eeg_subscriber = PikaSubscriber(self.rmq_address,
self.rmq_user, self.rmq_pwd)
self.eeg_subscriber.connect()
self.mu_publisher.connect()
self.mu_publisher.register(self.routing_keys[self.output_metric])
self.eeg_subscriber.register(self.routing_keys[self.input_metric])
def start(self):
_LOGGER.info("[Module %s] Starting Preprocessing. Routing "
"keys: %s" % (self.module_id, self.routing_keys))
self.eeg_subscriber.subscribe(self.routing_keys[self.input_metric],
self._preprocess)
def refit_ica(self):
t = Thread(target=self.eyeblinks_remover.fit, args=(self.eeg_data,))
t.start()
def _preprocess(self, ch, method, properties, body):
eeg = json.loads(body)
self.eeg_data = np.vstack([self.eeg_data, get_raw(eeg, self.num_channels)])
self.count += self.step_size
timestamp = eeg[-1]["timestamp"]
if self.enable_ica:
eeg = from_raw(self.eyeblinks_remover.transform(
get_raw(eeg, self.num_channels)), self.num_channels)
if ((self.count >= 5000 and not self.started_fit)
or self.count % 10000 == 0):
_LOGGER.info('refitting...')
self.started_fit = True
self.refit_ica()
processed_data = preprocess_stft(eeg, self.electrodes_placement)
data = {"timestamp": timestamp}
for key, value in processed_data.items():
data[key] = processed_data[key][-1]
_LOGGER.debug("--> output: %s" % data)
self.mu_publisher.publish(self.routing_keys[self.output_metric], data)
