shit

2025-10-25 14:07:31 -05:00
parent e1f23e9d35
commit c3b0526f59
10 changed files with 104620 additions and 1 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- a/pycache/compute_vector_feature.cpython-312.pyc
+++ b/pycache/compute_vector_feature.cpython-312.pyc
--- a/compute_vector_feature.py
+++ b/compute_vector_feature.py
@@ -0,0 +1,93 @@
+import numpy as np
+
+def compute_feature_vector(eegdata, Fs):
+    """Extract the features from the EEG
+        Inputs:
+          eegdata: array of dimension [number of samples, number of channels]
+          Fs: sampling frequency of eegdata
+
+        Outputs:
+          feature_vector: [number of features points;
+           number of different features]
+
+    """
+    # Delete last column (Status)
+    eegdata = np.delete(eegdata, -1, 1)
+
+    # 1. Compute the PSD
+    winSampleLength, nbCh = eegdata.shape
+
+    # Apply Hamming window
+    w = np.hamming(winSampleLength)
+    dataWinCentered = eegdata - np.mean(eegdata, axis=0) # Remove offset
+    dataWinCenteredHam = (dataWinCentered.T*w).T
+
+    NFFT = nextpow2(winSampleLength)
+    Y = np.fft.fft(dataWinCenteredHam, n=NFFT, axis=0)/winSampleLength
+    PSD = 2*np.abs(Y[0:NFFT/2,:])
+    f = Fs/2*np.linspace(0,1,NFFT/2)
+
+    # SPECTRAL FEATURES
+    # Average of band powers
+    # Delta <4
+    ind_delta, = np.where(f<4)
+    meanDelta = np.mean(PSD[ind_delta,:],axis=0)
+    # Theta 4-8
+    ind_theta, = np.where((f>=4) & (f<=8))
+    meanTheta = np.mean(PSD[ind_theta,:],axis=0)
+    # Low alpha 8-10
+    ind_alpha, = np.where((f>=8) & (f<=10))
+    meanLowAlpha = np.mean(PSD[ind_alpha,:],axis=0)
+    # Medium alpha
+    ind_alpha, = np.where((f>=9) & (f<=11))
+    meanMedAlpha = np.mean(PSD[ind_alpha,:],axis=0)
+    # High alpha 10-12
+    ind_alpha, = np.where((f>=10) & (f<=12))
+    meanHighAlpha = np.mean(PSD[ind_alpha,:],axis=0)
+    # Low beta 12-21
+    ind_beta, = np.where((f>=12) & (f<=21))
+    meanLowBeta = np.mean(PSD[ind_beta,:],axis=0)
+    # High beta 21-30
+    ind_beta, = np.where((f>=21) & (f<=30))
+    meanHighBeta = np.mean(PSD[ind_beta,:],axis=0)
+    # Alpha 8 - 12
+    ind_alpha, = np.where((f>=8) & (f<=12))
+    meanAlpha = np.mean(PSD[ind_alpha,:],axis=0)
+    # Beta 12-30
+    ind_beta, = np.where((f>=12) & (f<=30))
+    meanBeta = np.mean(PSD[ind_beta,:],axis=0)
+
+    feature_vector = np.concatenate((meanDelta, meanTheta, meanLowAlpha, meanHighAlpha,
+                                     meanLowBeta, meanHighBeta,
+                                     meanDelta/meanBeta, meanTheta/meanBeta,
+                                     meanAlpha/meanBeta, meanAlpha/meanTheta),axis=0)
+
+    feature_vector = np.log10(feature_vector)
+
+    return feature_vector
+
+def feature_names(ch_names):
+    """
+        Generate the name of the features
+
+        Arguments
+    ch_names: List with Electrode names
+    """
+
+    bands = ['pwr-delta', 'pwr-theta', 'pwr-low-alpha', 'pwr-high-alpha',
+             'pwr-low-beta', 'pwr-high-beta',
+             'pwr-delta/beta', 'pwr-theta/beta', 'pwr-alpha/beta', 'pwr-alpha-theta']
+    feat_names = []
+    for band in bands:
+        for ch in range(0,len(ch_names)-1):
+        #Last column is ommited because it is the Status Channel
+            feat_names.append(band + '-' + ch_names[ch])
+
+    return feat_names
+
+def nextpow2(i):
+    """ Find the next power of 2 for number i """
+    n = 1
+    while n < i:
+        n *= 2
+    return n
--- a/data/.DS_Store
+++ b/data/.DS_Store
--- a/data/hasini/brain_1761337390.csv
+++ b/data/hasini/brain_1761337390.csv
--- a/data/hasini/brain_1761337696.csv
+++ b/data/hasini/brain_1761337696.csv
--- a/data/hasini/brain_1761337804.csv
+++ b/data/hasini/brain_1761337804.csv
--- a/main.ipynb
+++ b/main.ipynb
@@ -0,0 +1,47 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d5051d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from compute_vector_feature import *\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56752746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sampling rate\n",
+    "Fs = 256"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,5 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12.11"
 dependencies = [
+    "ipykernel>=7.0.1",
    "muselsl>=2.3.1",
+    "numpy>=2.3.3",
 ]
--- a/uv.lock
+++ b/uv.lock
@@ -2,6 +2,24 @@ version = 1
 revision = 3
 requires-python = ">=3.12.11"

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