Automatic Reference Selection for Quantitative EEG Component Interpretation: Cross Spectrum Analysis Based on Bipolar EEG

نویسندگان

  • Bei Wang
  • Xingyu Wang
  • Akio Ikeda
  • Takashi Nagamine
  • Hiroshi Shibasaki
  • Takenao Sugi
  • Masatoshi Nakamura
چکیده

Automatic Electroencephalography (EEG) interpretation system had been developed as an important tool for the inspection of brain diseases. In this study, an automatic reference selection technique was developed to obtain the appropriate derivation for EEG components interpretation. The cross spectrum of bipolar EEG was adopted to detect the phase reversal among the EEG channels covering the scalp of head. Appropriate reference was selected automatically based on the detected phase reversal. Finally, a referential derivation was constructed. The distribution of EEG components was analyzed based on the constructed referential derivation to evaluate the effectiveness of selected reference for quantitative EEG component interpretation.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A review on EEG based brain computer interface systems feature extraction methods

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

متن کامل

A review on EEG based brain computer interface systems feature extraction methods

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

متن کامل

Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

متن کامل

Brain complexity increases during the manic episode of bipolar mood disorder type I

Fractal dimension of the electroencephalographic (EEG) signal has been argued to reflect the complexity of the underlying brain processes. To this date, conventional studies of EEG in mood disorders have not been able to distinguish between patients and normal individuals. Here we show that, compared to normal subjects, EEG fractal dimension is significantly augmented in the manic episode of bi...

متن کامل

Brain complexity increases during the manic episode of bipolar mood disorder type I

Fractal dimension of the electroencephalographic (EEG) signal has been argued to reflect the complexity of the underlying brain processes. To this date, conventional studies of EEG in mood disorders have not been able to distinguish between patients and normal individuals. Here we show that, compared to normal subjects, EEG fractal dimension is significantly augmented in the manic episode of bi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010