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)
_ELECTRODES_PLACEMENT = {"channel_0": {"main": "channel_0", "artifact": []}}
_BUFFER_SIZE = 128
with open("neurosky.csv", "rb") as inputFile:
with open("mu_neurosky_converted.csv", "wb") as outputFile:
writer = csv.writer(outputFile)
writer.writerow(["x", "y", "label"])
writer.writerow(["float", "float", "int"])
writer.writerow(["","","C"])
reader = csv.reader(inputFile)
headers = reader.next()
eeg_buffer = []
for row in reader:
x = row[0]
raw_eeg = row[4]
label = row[5]
eeg = {"timestamp": x, "channel_0": raw_eeg}
if len(eeg_buffer) > _BUFFER_SIZE:
processed_data = preprocess_stft(eeg_buffer, _ELECTRODES_PLACEMENT)
eeg_buffer = []
y = processed_data["channel_0"][-1]
writer.writerow([x,y,label])
else:
eeg_buffer.append(eeg)
