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Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

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Title: Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification
Authors: Kam, Tae-Eui1 kamte@korea.ac.kr, Suk, Heung-Il1 hisuk@korea.ac.kr, Lee, Seong-Whan2 sw.lee@korea.ac.kr
Source: Neurocomputing. May2013, Vol. 108, p58-68. 11p.
Subjects: Electroencephalography, Beamforming, Mathematical optimization, Image processing, Evoked potentials (Electrophysiology), Neurons, Statistics
Abstract: Abstract: Neuronal power attenuation or enhancement in specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), respectively, is a major phenomenon in brain activities involved in imaginary movement of body parts. However, it is known that the nature of motor imagery-related electroencephalogram (EEG) signals is non-stationary and highly variable over time and frequency. In this paper, we propose a novel method of finding a discriminative time- and frequency-dependent spatial filter, which we call ‘non-homogeneous filter.’ We adaptively select bases of spatial filters over time and frequency. By taking both temporal and spectral features of EEGs in finding a spatial filter into account it is beneficial to be able to consider non-stationarity of EEG signals. In order to consider changes of ERD/ERS patterns over the time–frequency domain, we devise a spectrally and temporally weighted classification method via statistical analysis. Our experimental results on the BCI Competition IV dataset II-a and BCI Competition II dataset IV clearly presented the effectiveness of the proposed method outperforming other competing methods in the literature. [Copyright &y& Elsevier]
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Database: Engineering Source
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Abstract:Abstract: Neuronal power attenuation or enhancement in specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), respectively, is a major phenomenon in brain activities involved in imaginary movement of body parts. However, it is known that the nature of motor imagery-related electroencephalogram (EEG) signals is non-stationary and highly variable over time and frequency. In this paper, we propose a novel method of finding a discriminative time- and frequency-dependent spatial filter, which we call ‘non-homogeneous filter.’ We adaptively select bases of spatial filters over time and frequency. By taking both temporal and spectral features of EEGs in finding a spatial filter into account it is beneficial to be able to consider non-stationarity of EEG signals. In order to consider changes of ERD/ERS patterns over the time–frequency domain, we devise a spectrally and temporally weighted classification method via statistical analysis. Our experimental results on the BCI Competition IV dataset II-a and BCI Competition II dataset IV clearly presented the effectiveness of the proposed method outperforming other competing methods in the literature. [Copyright &y& Elsevier]
ISSN:09252312
DOI:10.1016/j.neucom.2012.12.002