Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition
نویسندگان
چکیده
منابع مشابه
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 کاملMultivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization
Quantifying the phase synchrony between signals is important in many different applications, including the study of the chaotic oscillators in physics and the modeling of the joint dynamics between channels of brain activity recorded by electroencephalogram (EEG). Current measures of phase synchrony rely on either the wavelet transform or the Hilbert transform of the signals and suffer from con...
متن کاملClassification of Features of Pavement Profiles Using Empirical Mode Decomposition
The Long-Term Pavement Performance (LTPP) database contains surface profile data for numerous pavements that are used mainly for computing International Roughness Index (IRI).(2) In order to obtain more information from these surface profiles, a Hilbert-Huang Transform (HHT) based surface profile algorithm was developed to analyze LTPP field road profile data in order to extract smoothed, consi...
متن کاملA Low Cost Eeg Based Bci Prosthetic Using Motor Imagery
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source...
متن کاملMultivariate empirical mode decomposition and application to multichannel filtering
Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of monoand multivariate signals without any change in the core of the algorithm. Qualit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Systems and Rehabilitation Engineering
سال: 2013
ISSN: 1534-4320,1558-0210
DOI: 10.1109/tnsre.2012.2229296