test_output.py

import numpy as np
import pyxdf
from matplotlib import pyplot as plt

# File to test
TEST_FILE = 'C:\\Recordings\\CurrentStudy\\exp4\\untitled.xdf'

# Load it and get the channels
data = pyxdf.load_xdf(TEST_FILE)
channels = data[0][0]['info']['desc'][0]['channels'][0]

# Get all the channel names
channames = []
for channel in channels['channel']:
	print(channel)
	channames.append(channel['label'][0])
print(channames)

# Get the channel for the Mindwave raw data in `rawEeg`
raw_channel = "Fp1"
chanind = [c for c,n in enumerate(channames) if n == raw_channel][0]
print("rawEeg channel index: %s" % chanind)

# Gather all the rawEeg data and plot it
timeseries = data[0][0]['time_series']
raweeg = []
for row in timeseries:
	if np.isnan(row[chanind]): continue
	raweeg.append(row[chanind])

timestamps = data[0][0]['time_stamps']
srate = len(timestamps)/(timestamps[-1]-timestamps[0])
print("Sampling rate: %s " % srate)

# Plot the raw data
plt.figure()
plt.plot(raweeg)

# Plot a spectrogram (time-frequency decomposition) of the data
plt.figure()
spectrum, freqs, t, im = plt.specgram(raweeg, Fs=srate, detrend='mean')
plt.ylim([0,120])
plt.clim([-20,20])
plt.show()