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

Forrest Sheng Bao,Xin Liu,Christina Zhang

doi:10.1155/2011/406391

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.

Highlights

Computer-aided diagnosis based on EEG has become possible in the last decade for several neurological diseases such as Alzheimer’s disease [1, 2] and epilepsy [3, 4]
Researchers have developed systems [3, 4, 7, 8] that can hopefully use random interictal EEG records for epilepsy diagnosis in instances that are difficult for physicians to make diagnostic decisions with their naked eyes
An open source tool that can extract EEG features would benefit the computational neuroscience community since feature extraction is repeatedly invoked in the analysis of EEG signals

Summary

Introduction

Computer-aided diagnosis based on EEG has become possible in the last decade for several neurological diseases such as Alzheimer’s disease [1, 2] and epilepsy [3, 4]. An open source tool that can extract EEG features would benefit the computational neuroscience community since feature extraction is repeatedly invoked in the analysis of EEG signals. Because of Python’s increasing popularity in scientific computing, and especially in computational neuroscience, a Python module for EEG feature extraction would be highly useful. We have developed PyEEG, a Python module for EEG feature extraction, and have tested it in our previous epileptic EEG research [3, 8, 11]. Though originally designed for EEG, PyEEG can be used to analyze other physiological signals that can be treated as time series, especially MEG signals that represent the magnetic fields induced by currents of neural electrical activities.

Main Framework

Supported Feature Extraction

Using PyEEG on Real Data

Discussion and Future