Moved correlation analysis to a script and added new videos

lab7/analysis/analysis.py

@@ -0,0 +1,67 @@
+from scipy import signal, stats
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import argparse
+import sys
+
+## sampling rate of ganglion is 200Hz
+fs = 200.0
+
+def extract_data(fname):
+    d = pd.read_csv(fname)
+    eeg = np.array(d.ix[:, 1:5])
+    tag = np.array(d.Marker)
+    start = np.where(tag == 1)[0][0]
+    end = np.where(tag == 2)[0][0]
+    eeg = eeg[start:end]
+    return eeg
+
+## get data
+eeg1 = extract_data(sys.argv[1])
+eeg2 = extract_data(sys.argv[2])
+
+## data may be off by a few samples, so need to align
+N_samp = min(len(eeg1), len(eeg2))
+eeg1 = eeg1[:N_samp]
+eeg2 = eeg2[:N_samp]
+
+## filter signal to remove noise
+b, a = signal.butter(2, (2/(fs/2), 20/(fs/2)), btype='bandpass')
+eeg1 = signal.filtfilt(b, a, eeg1, axis=0)
+eeg2 = signal.filtfilt(b, a, eeg2, axis=0)
+
+## advance window of 200 samples
+## take correlation between signals across each sample
+window = 200
+step = 25
+corr = []
+times = []
+
+for start in np.arange(0, N_samp, step):
+    end = start + window
+    w1 = eeg1[start:end]
+    w2 = eeg2[start:end]
+
+    ## average the correlation across each channel
+    r = 0
+    for c in range(w1.shape[1]):
+        r += stats.pearsonr(w1[:, c], w2[:, c])[0]
+    r /= w1.shape[1]
+
+    mid = (start+end)/2 # middle sample
+    t = mid / fs # convert middle sample to time
+
+
+    times.append(t)
+    corr.append(r)
+
+times = np.array(times)
+corr = np.array(corr)
+
+plt.figure(figsize=(14,4))
+plt.plot(times, np.abs(corr))
+plt.xlabel('Time (s)')
+plt.ylabel('Correlation')
+plt.title('Correlation across brains')
+plt.show()

lab7/analysis/cross_brain_correlation.ipynb

@@ -76,7 +76,9 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "## filter signal to remove noise\n",
@@ -181,7 +183,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,