Harmonicity Metrics Computation on MNE Epochs File

In this notebook, we demonstrate how to compute harmonicity metrics from an MNE epochs file using the Biotuner library. Harmonicity metrics provide insights into the periodic structure of EEG signals and can be useful for various applications in neuroscience research. Biotuner is a powerful tool for extracting these metrics by analyzing the harmonic architecture of M/EEG data.

# Import necessary modules
import mne
from mne.datasets import multimodal
from mne import make_fixed_length_events
from biotuner.biotuner2d import biotuner_mne

# Define the biotuner parameters
bt_dict = {
    'peaks_function': 'EMD',
    'precision': 1,
    'fmin': 2,
    'fmax': 90,
    'n_peaks': 5,
    'harm_thresh': 30,
    'delta_lim': 100,
}

# Path to the multimodal data
data_path = multimodal.data_path()

# Filepath to the multimodal data
raw_file = str(data_path.joinpath('multimodal_raw.fif'))

# Suppress output
mne.set_log_level('ERROR')

# Load the raw data
raw = mne.io.read_raw_fif(raw_file, preload=True)

# Filter the data
raw.filter(l_freq=2., h_freq=90.)

# Get the names of the MEG channels
meg_channels = mne.pick_types(raw.info, meg=True)

# Keep only the first 10 MEG channels
meg_channels = meg_channels[:10]

# Update 'sf' in the bt_dict
bt_dict['sf'] = raw.info['sfreq']

# Create events
events = make_fixed_length_events(raw, start=0, stop=None, duration=2.0)

# Epoch the data
tmin = 0.0  # start of each epoch (0s before the trigger)
tmax = 2.0  # end of each epoch (2s after the trigger)

# Make sure only 10 epochs are created
if len(events) > 3:
    events = events[:3]

# Pick only the first 10 MEG channels
raw.pick_channels([raw.ch_names[i] for i in meg_channels])

# Epoch the data
epochs = mne.Epochs(raw, events, tmin=tmin, tmax=tmax, baseline=None, preload=True)

# Compute biotuner metrics on epochs file
bt_df = biotuner_mne(epochs, bt_dict, savefile=True, savename='multimodal_raw')

c:\users\user\github\biotuner\biotuner\metrics.py:659: RuntimeWarning: divide by zero encountered in double_scalars
  harm_temp.append(1 / delta_norm)
c:\Users\User\anaconda3\envs\biotuner\lib\site-packages\numpy\lib\function_base.py:380: RuntimeWarning: Mean of empty slice.
  avg = a.mean(axis)
c:\Users\User\anaconda3\envs\biotuner\lib\site-packages\numpy\core\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
c:\users\user\github\biotuner\biotuner\peaks_extraction.py:827: RuntimeWarning: invalid value encountered in true_divide
  coh /= temp2
c:\Users\User\anaconda3\envs\biotuner\lib\site-packages\numpy\core\fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,

bt_df

	trial	electrode	peaks_function	precision	fmin	fmax	n_peaks	harm_thresh	delta_lim	sf	...	sum_q_for_all_intervals	harm_sim	matrix_harm_sim	matrix_cons	matrix_denom	cons	tenney	harm_fit	harmsim	subharm_tension
0	0	0	EMD	1	2	90	5	30	100	600.61499	...	52	33.76	27.408042	0.331773	3078.000000	0.243590	2.914161	11	33.760684	[0.040245430660774836]
1	0	1	EMD	1	2	90	5	30	100	600.61499	...	28703	38.92	44.497354	0.623413	3325.200000	0.625000	3.035390	15	38.918388	[0.08681323594992725]
2	0	2	EMD	1	2	90	5	30	100	600.61499	...	14493	33.10	3.744248	0.038370	4463.000000	0.365224	5.114485	10	33.099289	[0.08750497897263645]
3	0	3	EMD	1	2	90	5	30	100	600.61499	...	373	23.85	10.532544	0.109504	61.000000	0.351923	3.408947	12	23.846154	[0.03407356717408203]
4	0	4	EMD	1	2	90	5	30	100	600.61499	...	6643025	31.47	13.313946	0.155624	3825.750000	0.268640	6.229364	5	31.474884	[0.1350282792829261]
5	0	5	EMD	1	2	90	5	30	100	600.61499	...	241	24.70	11.466942	0.119611	47.333333	0.440909	3.312544	12	24.696970	[0.03335222463092775]
6	0	6	EMD	1	2	90	5	30	100	600.61499	...	518689	12.02	8.476437	0.090718	3956.666667	0.343832	5.180535	11	12.015883	[0.11689849920521807]
7	0	7	EMD	1	2	90	5	30	100	600.61499	...	29897	36.77	11.390282	0.119260	5422.500000	0.433333	4.150084	9	36.774950	[0.09721535992779774]
8	0	8	EMD	1	2	90	5	30	100	600.61499	...	1453	21.22	13.350769	0.146396	53.466667	0.472394	4.368141	9	21.222480	[0.0945973868157042]
9	0	9	EMD	1	2	90	5	30	100	600.61499	...	518	40.59	12.854671	0.135105	59.000000	0.325490	3.763756	9	40.588235	[0.08667506229176597]
10	1	0	EMD	1	2	90	5	30	100	600.61499	...	6294041	13.19	9.868479	0.107444	3310.133333	0.411584	4.694222	10	13.188127	[0.094220521933636]
11	1	1	EMD	1	2	90	5	30	100	600.61499	...	83	42.68	40.753629	0.579590	2516.533333	0.424242	3.309644	14	42.676768	[0.0950166640113226]
12	1	2	EMD	1	2	90	5	30	100	600.61499	...	2663695	30.63	6.615343	0.069626	2461.333333	0.357101	5.246129	10	30.633896	[0.11812990118576759]
13	1	3	EMD	1	2	90	5	30	100	600.61499	...	362	54.89	33.688886	0.419370	2359.972222	0.419048	4.273919	9	54.894180	[0.12751767843497627]
14	1	4	EMD	1	2	90	5	30	100	600.61499	...	7008125	13.71	8.305882	0.088653	3880.666667	0.378200	5.334104	10	13.711387	[0.12551460309881624]
15	1	5	EMD	1	2	90	5	30	100	600.61499	...	246	35.98	8.321897	0.087179	4186.500000	0.442495	3.967141	12	35.983187	[0.16609746768320133]
16	1	6	EMD	1	2	90	5	30	100	600.61499	...	34970	36.71	27.389032	0.352668	3735.533333	0.264411	5.394392	9	36.705859	[0.1402171529768576]
17	1	7	EMD	1	2	90	5	30	100	600.61499	...	2967794	30.64	6.704366	0.070518	3414.166667	0.390667	4.125512	11	30.639777	[0.0816930528050395]
18	1	8	EMD	1	2	90	5	30	100	600.61499	...	59	56.67	23.393769	0.258938	3471.000000	0.397917	3.593157	10	56.666667	[0.12140059506352861]
19	1	9	EMD	1	2	90	5	30	100	600.61499	...	5917909	10.10	6.558216	0.069720	3939.133333	0.352273	6.133720	7	10.097506	[0.16524134542296162]
20	2	0	EMD	1	2	90	5	30	100	600.61499	...	349	32.82	25.944670	0.292576	3368.600000	0.209524	3.750084	15	32.817460	[0.15660936139458068]
21	2	1	EMD	1	2	90	5	30	100	600.61499	...	1826	31.06	14.386101	0.160324	3061.888889	0.275084	5.297506	6	31.055556	[0.1443963218278032]
22	2	2	EMD	1	2	90	5	30	100	600.61499	...	66	46.67	24.512396	0.272902	1656.333333	0.408333	3.427142	10	46.666667	[0.10506273525832888]
23	2	3	EMD	1	2	90	5	30	100	600.61499	...	843	20.02	12.219981	0.129773	37.933333	0.424866	4.347529	10	20.022282	[0.08233474456933955]
24	2	4	EMD	1	2	90	5	30	100	600.61499	...	2745255	19.83	21.547547	0.295447	3129.266667	0.210909	3.705701	14	19.831489	[0.09423720971902239]
25	2	5	EMD	1	2	90	5	30	100	600.61499	...	232	35.39	8.155326	0.085397	3795.500000	0.410833	4.125512	11	35.388889	[0.044989875320305316]
26	2	6	EMD	1	2	90	5	30	100	600.61499	...	124	38.43	35.991951	0.465078	3027.133333	0.476190	2.990912	11	38.425926	[0.13364980249370828]
27	2	7	EMD	1	2	90	5	30	100	600.61499	...	8296287	30.73	20.636476	0.247780	3694.777778	0.336508	4.101061	11	30.734219	[0.14876591904748074]
28	2	8	EMD	1	2	90	5	30	100	600.61499	...	880809	30.21	6.031381	0.063156	3972.833333	0.256258	3.993197	12	30.206086	[0.10044430391744703]
29	2	9	EMD	1	2	90	5	30	100	600.61499	...	2490511	13.44	13.450237	0.151170	4338.666667	0.399194	3.974835	10	13.439635	[0.11772585065374275]

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