# -*- coding: utf-8 -*-
import pandas as pd
from ..signal import signal_power
from .utils import _sanitize_eeg
[docs]
def eeg_power(
eeg, sampling_rate=None, frequency_band=["Gamma", "Beta", "Alpha", "Theta", "Delta"], **kwargs
):
"""**EEG Power in Different Frequency Bands**
See our `walkthrough <https://neuropsychology.github.io/NeuroKit/examples/eeg_power/eeg_power.html>`_ for
details.
* **Gamma** (30-80 Hz)
* **Beta** (13-30 Hz)
* **Beta 1** (13-16 Hz)
* **Beta 2** (16-20 Hz)
* **Beta 3** (20-30 Hz)
* **SMR** (13-15 Hz)
* **Alpha** (8-13 Hz)
* **Mu** (9-11 Hz)
* **Theta** (4-8 Hz)
* **Delta** (1-4 Hz)
Parameters
----------
eeg : array
An array (channels, times) of M/EEG data or a Raw or Epochs object from MNE.
sampling_rate : int
The sampling frequency of the signal (in Hz, i.e., samples/second). Only necessary if
smoothing is requested.
frequency_band : list
A list of frequency bands (or tuples of frequencies).
**kwargs
Other arguments to be passed to ``nk.signal_power()``.
Returns
-------
pd.DataFrame
The power in different frequency bands for each channel.
Examples
---------
.. ipython:: python
import neurokit2 as nk
# Raw objects
eeg = nk.mne_data("raw")
by_channel = nk.eeg_power(eeg)
by_channel.head()
average = by_channel.mean(numeric_only=True, axis=0)
average["Gamma"]
References
----------
- Lehmann, D., & Skrandies, W. (1980). Reference-free identification of components of
checkerboard-evoked multichannel potential fields. Electroencephalography and clinical
neurophysiology, 48(6), 609-621.
"""
# Sanitize names and values
frequency_band, band_names = _eeg_power_sanitize(frequency_band)
# Sanitize input
eeg, sampling_rate, _ = _sanitize_eeg(eeg, sampling_rate=sampling_rate)
# Iterate through channels
data = []
for channel in eeg.columns:
rez = signal_power(
eeg[channel].values,
sampling_rate=sampling_rate,
frequency_band=frequency_band,
**kwargs,
)
data.append(rez)
data = pd.concat(data, axis=0)
data.columns = band_names
data.insert(0, "Channel", eeg.columns)
data.reset_index(drop=True, inplace=True)
return data
# =============================================================================
# Utilities
# =============================================================================
def _eeg_power_sanitize(frequency_band=["Gamma", "Beta", "Alpha", "Theta", "Delta"]):
band_names = frequency_band.copy() # This will used for the names
for i, f in enumerate(frequency_band):
if isinstance(f, str):
f_name = f.lower()
if f_name == "gamma":
frequency_band[i] = (30, 80)
elif f_name == "beta":
frequency_band[i] = (13, 30)
elif f_name == "beta1":
frequency_band[i] = (13, 16)
elif f_name == "beta2":
frequency_band[i] = (16, 20)
elif f_name == "beta3":
frequency_band[i] = (20, 30)
elif f_name == "smr":
frequency_band[i] = (13, 15)
elif f_name == "alpha":
frequency_band[i] = (8, 13)
elif f_name == "mu":
frequency_band[i] = (9, 11)
elif f_name == "theta":
frequency_band[i] = (4, 8)
elif f_name == "delta":
frequency_band[i] = (1, 4)
else:
raise ValueError(f"Unknown frequency band: '{f_name}'")
elif isinstance(f, tuple):
band_names[i] = f"Hz_{f[0]}_{f[1]}"
else:
raise ValueError("'frequency_band' must be a list of tuples (or strings).")
return frequency_band, band_names
