Examples:ECG-Analysis+EEGSynth (#309)

* ecg analysis script

* eegsynth application example

---------

Co-authored-by: caurnhammer <christoph@mentalab.com>

examples/eegsynth_demo/buffer.ini

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+[general]
+debug=2
+delay=0.010
+
+[redis]
+hostname=localhost
+port=6379
+
+[fieldtrip]
+port=1972,1973,1974

examples/eegsynth_demo/historycontrol.ini

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+[general]
+debug=2
+
+[redis]
+hostname=localhost
+port=6379
+
+[history]
+window=10
+; window length for smoothing (s)
+stepsize=0.05
+; update time (s)
+
+[input]
+; control values to plot, separated by comma
+freeze=0
+channels=spectral.channel1.alpha,spectral.channel2.theta,spectral.channel2.beta

examples/eegsynth_demo/lsl2ft.ini

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+[general]
+debug=1
+
+[fieldtrip]
+hostname=localhost
+port=1972
+
+[redis]
+hostname=localhost
+port=6379
+
+[lsl]
+; this can be used to select the desired stream (in case there are multiple)
+name=
+type=ExG
+timeout=30  ; in seconds

examples/eegsynth_demo/outputmidi_loopback.ini

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+[general]
+debug=1
+delay=0.05
+monophonic=1 ; boolean
+
+[redis]
+hostname=localhost
+port=6379
+
+[midi]
+device=IAC Driver Bus 1
+channel=1
+
+[control]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+
+[trigger]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+note=post.channel1.alpha
+start=post.channel1.alpha
+
+[duration]
+note=0.5      ; the note will be switched off after the specified time (in seconds)
+
+[velocity]
+
+[scale]
+; scale and offset can be used to map Redis values to MIDI values between 0 to 127
+; the default scale for all channels is 127
+
+[offset]
+; the default offset for all channels is 0
+note=40

examples/eegsynth_demo/outputmidi_loopback_2.ini

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+[general]
+debug=1
+delay=0.05
+monophonic=1 ; boolean
+
+[redis]
+hostname=localhost
+port=6379
+
+[midi]
+device=IAC Driver Bus 2
+channel=1
+
+[control]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+
+[trigger]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+note=post.channel2.thetabetaratio
+start=post.channel2.thetabetaratio
+
+[duration]
+note=0.5      ; the note will be switched off after the specified time (in seconds)
+
+[velocity]
+
+[scale]
+; scale and offset can be used to map Redis values to MIDI values between 0 to 127
+; the default scale for all channels is 127
+
+[offset]
+; the default offset for all channels is 0
+note=40

examples/eegsynth_demo/postprocessing.ini

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+[general]
+delay=0.05
+debug=2
+
+[redis]
+hostname=localhost
+port=6379
+
+[initial]
+; here you can specify the initial values of some control values
+
+[input]
+; the keys here can have an arbitrary name, but should map those in the output section
+; the keys must be lower-case. values should not contain an equation, only one-to-one mappings
+alpha_1=spectral.channel1.alpha
+theta_2=spectral.channel2.theta
+beta_2=spectral.channel2.beta
+
+[output]
+; besides +, -, /, *, the equations also support log, log2, log10, exp, power from numpy
+; and compress, limit, rescale, normalizerange, normalizestandard from EEGsynth
+post.channel1.alpha = limit(alpha_1 / 1750, 0, 0.8)
+post.channel2.thetabetaratio = limit(theta_2/beta_2 / 6, 0, 0.9)

examples/eegsynth_demo/preprocessing.ini

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+[general]
+delay=0.10
+debug=1
+
+[redis]
+hostname=localhost
+port=6379
+
+[input_fieldtrip]
+hostname=localhost
+port=1972
+timeout=30
+
+[output_fieldtrip]
+hostname=localhost
+port=1973
+
+[processing]
+window=0.12
+;smoothing=0.2
+reference=none
+lowpassfilter=45
+highpassfilter=2
+filterorder=511
+;downsample=1
+notchfilter=50
+
+[scale]
+highpassfilter=1
+lowpassfilter=1
+notchfilter=1
+
+[offset]
+highpassfilter=0
+lowpassfilter=0
+notchfilter=0

examples/eegsynth_demo/push2lsl.py

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+import explorepy
+
+expdev = explorepy.Explore()
+expdev.connect("Explore_AAAI")
+expdev.push2lsl()

examples/eegsynth_demo/spectral.ini

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+[general]
+debug=2
+delay=0.1
+
+[redis]
+hostname=localhost
+port=6379
+
+[fieldtrip]
+hostname=localhost
+port=1973
+timeout=30
+
+[input]
+; this specifies the channels from the FieldTrip buffer
+; the channel names (on the left) can be specified as you like
+channel1=1
+channel2=2
+; channel3=3
+; channel4=4
+; channel5=5
+; channel6=6
+; channel7=7
+; channel8=8
+
+[processing]
+; the sliding window is specified in seconds
+window=5.0
+
+[scale]
+window=1
+
+[offset]
+window=0
+
+[band]
+; the frequency bands can be specified as you like, but must be all lower-case
+; you should give the lower and upper range of each band
+delta=2-5
+theta=5-8
+alpha=8-12
+beta=12-30
+gamma=35-45
+
+[output]
+; the results will be written to Redis as "spectral.channel1.alpha" etc.
+prefix=spectral

examples/wiki-ecg-analysis.py

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+import neurokit2 as nk
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pyedflib
+
+def edf_to_arr(edf_path):
+    f = pyedflib.EdfReader(edf_path)
+    n = f.signals_in_file
+    signal_labels = f.getSignalLabels()
+    sigbufs = np.zeros((n, f.getNSamples()[0]))
+    for i in np.arange(n):
+        sigbufs[i, :] = f.readSignal(i)
+
+    return sigbufs
+
+data = edf_to_arr("../data/wiki-ECG-resting_ExG.bdf") # read in bdf file
+data = data[1] # channel 1 holds ECG data
+data *= 1e-3 # scale uV to mV for ECG analysis in neurokit
+ecg_signals, info = nk.ecg_process(data[5000:25000], sampling_rate=250) # convert 
+
+rpeaks = info["ECG_R_Peaks"]
+cleaned_ecg = ecg_signals["ECG_Clean"]
+
+intervalrelated = nk.ecg_intervalrelated(ecg_signals)
+intervalrelated.iloc[0,1:83]
+
+nk.ecg_plot(ecg_signals, info)
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/resting_ecg.png")
+
+ecg_signals, info = nk.ecg_process(data[18000:20000] , sampling_rate=250) # take a subset from the middle of recording and apply neurokit
+rpeaks = info["ECG_R_Peaks"]
+cleaned_ecg = ecg_signals["ECG_Clean"]
+plot = nk.events_plot(rpeaks, cleaned_ecg[0:cleaned_ecg.shape[0]])
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/resting_rpeaks.png")
+
+peaks, info = nk.ecg_peaks(data[5000:25000], sampling_rate=250)
+hrv_time = nk.hrv_time(peaks, sampling_rate=250, show=True)
+hrv_time
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/sv_ecg_time.png")