Article thumbnail

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

By Forrest Sheng Bao, Xin Liu and Christina Zhang

Abstract

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction

Topics: Research Article
Publisher: Hindawi Publishing Corporation
OAI identifier: oai:pubmedcentral.nih.gov:3070217
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (2001). A .A .P e t r o s i a n ,D .V .P r o k h o r o v ,W .L a j a r a - N a n s o n ,a n d
  2. (2006). A .B .G a r d n e r ,A .M .K r i e g e r ,G .V a c h t s e v a n o s ,a n dB . Litt, “One-class novelty detection for seizure analysis from intracranial EEG,”
  3. (2009). A combined linear & nonlinear approach for classification of epileptic EEG signals,”
  4. (2008). A comparative study of synchrony measures for the early detection of Alzheimer’s disease based on EEG,”
  5. (2001). a n dC .E .E l g e r ,“ I n d i c a t i o n so fn o n l i n e a rd e t e r m i n i s t i ca n d finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state,” Physical Review E,
  6. (2009). a n g ,a n dK
  7. A paradigm for epileptic seizure prediction using a coupled oscillator model of the brain,”
  8. (1988). Approach to an irregular time series on the basis of the fractal theory,”
  9. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks,”
  10. (2008). B a o ,D .Y .C .L i e ,a n dY .Z h a n g ,“ An e wa p p r o a c ht o automated epileptic diagnosis using EEG and probabilistic neural network,”
  11. (1995). e n g ,S .H a v l i n ,H .E .S t a n l e y ,a n dA .L .G o l d b e r g e r , “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series,” Chaos,v o l .5 ,n o .
  12. (1970). EEG analysis based on time domain properties,”
  13. (1991). K.Shinosaki,H.Sakamotoet al.,“Quantificationof EEG irregularity by useoftheentropy ofthepower spectrum,”
  14. (1995). Kolmogorovcomplexity of finite sequences and recognition of different preictal EEG patterns,”
  15. (2009). Localization of seizure onset area from intracranial non-seizure EEG by exploiting locally enhanced synchrony,”
  16. (2010). Performance of dynamic features in classifying scalp epileptic interictal and normal EEG,”
  17. (1997). Rabinowicz,“Searching for hidden informationwith gabor transform in generalized tonic-clonic seizures,” ElectroencephalographyandClinicalNeurophysiology,vol.103,no.4,pp.
  18. (2003). Seizure prediction by nonlinear EEG analysis,”
  19. (1999). Temporal and spatial complexity measures for electroencephalogram based braincomputer interfacing,”