Skip to content

unlikeghost/KaraOneTools

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

KaraOne Tools

KaraOne Tools es una librería de Python que nos permitirá trabajar con el dataset de KaraOne EEG de una maneta más sencilla

Instalación

Clona este repositorio e instala las dependencias

git clone https://github.com/unlikeghost/KaraOneTools.git
pip install -r requirements.txt

Uso

Descarga del dataset

from KaraOne import Downloader

test = Downloader()
test.downlad("MM05")
test.extract("MM05")

Aumento de datos

import matplotlib.pyplot as plt
from KaraOne import DataAugmentation    

eeg_fake = np.random.randint(0, 100, size=(3, 10, 50))
    
labels = np.array(list(range(0, 3)))
augmentation_factor = 3
    
DA = DataAugmentation(eeg_fake, labels, augmentation_factor)
    
x_jitter, labels_jitter, ident_jitter = DA.jitter(low_sigma=5, high_sigma=6)
    
plt.plot(x_jitter[0, 0, 0:5], label=f'{ident_jitter[0]}__target_{labels_jitter[0]}')
plt.plot(x_jitter[3, 0, 0:5], label=ident_jitter[3])
    
plt.title('EEG con jitter (Ejemplo)')
plt.legend()
plt.show()

x_scale, labels_scale, ident_scale = DA.scaling(low_sigma=1, high_sigma=2)
    
plt.plot(x_scale[0, 0, 0:5], label=f'{ident_scale[0]}__target_{labels_scale[0]}')
plt.plot(x_scale[3, 0, 0:5], label=ident_scale[3])
    
plt.title('EEG con Scaling (Ejemplo)')
plt.legend()
plt.show()

Separación de datos

from KaraOne import SplitData
test = SplitData(subject='MM05',
                     type_action='thinking_inds',
                     ignore_channels=['VEO', 'HEO','EKG', 'EMG','Trigger'],
                     keep_channels=['T7', 'C5', 'C3', 'CP5', 'CP3', 'CP1', 'P3', 'C4', 'FC6', 'FT8'],
                     root_folder='Data',
                     src_folder='RawDataExtracted',
                     dst_folder='SplittedData')

test.split(duration_time=4400, save=True)

Transformada de wavelet

from KaraOne import Downloader

wavelet = Wavelet(subject='MM05',
                  desc_level=6,
                  root_folder='Data',
                  src_folder='SplittedData',
                  dst_folder='WaveldData')

wavelet.apply(scale=True, save=True)

Autor

About

Herramientas para el uso de KaraOne

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages