SOBI-RO for Automatic Removal of Electroocular Artifacts from EEG Data-Based Motor Imagery

نویسندگان

  • Arjon Turnip
  • Fajar Budi Utomo
چکیده

Signals from eye movements and blinks can be orders of magnitude larger than braingenerated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG datain amotor imagery experiment. BBS is a signalprocessing methodology that includes independent component analysis (ICA)using second order blind identification with robust orthogonalization (SOBI-RO) is proposed.Simulation results shows that the ocular artifacts are significantly removed and the sources of the brain activity are clearly identified. The identification performance using signal to distortion ratio value about 68.88% is achieved. Keywords—EEG signal, Ocular Artifact, SOBI-RO.

برای دانلود رایگان 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

Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.

Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. ...

متن کامل

Ballistocardiogram artifacts in simultaneous EEG- fMRI acquisitions

The simultaneous acquisition of electroencephalograpy (EEG) and functional magnetic resonance imaging (fMRI) data is very promising for the study of cognitive processes and disorders but causes severe artifacts in the EEG. In this study the aim is to remove the ballistocardiogram artifact, caused by cardiac pulse-related movements of the electrodes in the magnetic field. For this purpose a meth...

متن کامل

Classification of EEG-based motor imagery BCI by using ECOC

AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...

متن کامل

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination o...

متن کامل

Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014