Individual alpha frequency in Hz

[art4style]

Hello, I am using the Mind Monitor app to analyze my brainwaves, specifically the alpha frequency range. Based on the logarithmic values provided by the app, I have calculated the absolute power values for the alpha range. However, I am unable to determine my individual alpha frequency in Hz. Can anyone please guide me on how to find my unique alpha frequency using the Mind Monitor app? Or maybe you can recommend other software compatible with Muse S. Thank you in advance for your help.

[Liv]

Hi, I have the same question. Have you figured it out how?

[NotoriousREV]

I’m also looking for this information and would be grateful if anyone could point me in the right direction.

[James]

Alpha is the sum of all power within the frequency range of 7.5Hz to 13Hz. So there is no such thing as a single "unique alpha frequency".
What you can look for, if you wish, is to see which of those discrete frequency bins has the highest power spectral density, and this is shown on the Discrete frequency graph:

alpha.png (114.66 KiB) Viewed 2775 times

The blue area is Alpha.

However, this is just for a single point in time..
If you wanted to calculate where the high point is over a large data set (e.g. a 30 minute meditation), then you can do this with a lot of math! You would need to set the recording interval to Constant in settings to get all the RAW EEG data at 256Hz, then perform an FFT on the entire data set. This will give you individual frequency power bins and then you could look to see which of the ones between 7.5Hz and 13Hz are the largest.

FFT math can be found here: https://rosettacode.org/wiki/Fast_Fourier_transform

[Liv]

Hi James,

Thanks for your feedback.
Do you think my function here is in the right direction?
I tried using the Peak Alpha Frequency (PAF) method to determine IAF.

import mne
from mne.time_frequency import psd_array_welch

# Function to find the peak alpha frequency
def find_peak_alpha(raw, channel_index):
data_segment = raw.get_data(picks=[channel_index])
n_fft = data_segment.shape[1]
psds, freqs = psd_array_welch(data_segment, sfreq=raw.info['sfreq'], fmin=8, fmax=13, n_fft=n_fft, n_per_seg=n_fft)
peak_index = np.argmax(psds)
peak_alpha_freq = freqs[peak_index]
return peak_alpha_freq

[James]

I'm not familiar with that library, sorry.