Title :
Processing movement related cortical potentials in EEG signals for identification of slow and fast movements
Author :
Riaz, Farhan ; Hassan, Asif ; Rehman, S. ; Niazi, I. ; Jochumsen, M. ; Dremstrup, K.
Author_Institution :
Coll. of Electr. & Mech. Eng., Nat. Univ. of Sci. & Technol. (NUST), Islamabad, Pakistan
Abstract :
The extraction of intended kinetic information from an EEG signal can have several applications related to the rehabilitation for subjects with various neurological disorders. However, the task is mainly constrained by the low signal-to-noise ratio for the EEG signals. It is well known that the cortical activity takes place at a very low frequency since it is characterized by the dropping of movement related cortical potential (MRCP) across the sampled EEG signal. The strong variations in the MRCP is indicative of the noise due to various sources. The aim of this work is to remove this noise from the EEG signals using empirical mode decomposition, which decomposes a signal into harmonics (intrinsic mode functions - IMF) of various frequencies. The IMFs pertaining to small frequencies are later used for features extraction where we extract the spatial and spectral features from the selected IMFs. The features are later used for classification using support vector machines (SVM). Our experiments show superior results to the benchmark method for the underlying dataset that has been used in this research.
Keywords :
bioelectric potentials; biomechanics; electroencephalography; feature extraction; medical disorders; medical signal processing; neurophysiology; patient rehabilitation; signal classification; support vector machines; EEG signals; MRCP; SVM; benchmark method; classification; cortical activity; empirical mode decomposition; fast movement identification; feature extraction; harmonics; intended kinetic information extraction; intrinsic mode functions; movement related cortical potential dropping; neurological disorder; processing movement; rehabilitation; signal decomposition; signal-to-noise ratio; slow movement identification; spatial features; spectral features; support vector machines; underlying dataset; Educational institutions; Electroencephalography; Feature extraction; Force; Mel frequency cepstral coefficient; Noise; Support vector machines;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6944724