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Temporal and spatial features of single-trial EEG for brain-computer interface.

Zhao Q, Zhang L - Comput Intell Neurosci (2007)

Bottom Line: Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features.Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate.The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. qbzhao@sjtu.edu.cn

ABSTRACT
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

No MeSH data available.


The event-related spectral perturbation (ERSP) showsmean event-related changes in spectral power at each time during the epoch andat each frequency. Intertrial coherence (ITC) indicates degree of that the EEGactivity at a given time and frequency in single trials are phase-locked (notphase-random with respect to the time-locking experimental event). (a) ERSP andITC of component 9 during left-hand movement imagery. (b) ERSP and ITC ofcomponent 9 during right-hand movement imagery. (c) ERSP and ITC of component19 during left-hand movement imagery. (d) ERSP and ITC of component 19 duringright-hand movement imagery.
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fig9: The event-related spectral perturbation (ERSP) showsmean event-related changes in spectral power at each time during the epoch andat each frequency. Intertrial coherence (ITC) indicates degree of that the EEGactivity at a given time and frequency in single trials are phase-locked (notphase-random with respect to the time-locking experimental event). (a) ERSP andITC of component 9 during left-hand movement imagery. (b) ERSP and ITC ofcomponent 9 during right-hand movement imagery. (c) ERSP and ITC of component19 during left-hand movement imagery. (d) ERSP and ITC of component 19 duringright-hand movement imagery.


Temporal and spatial features of single-trial EEG for brain-computer interface.

Zhao Q, Zhang L - Comput Intell Neurosci (2007)

The event-related spectral perturbation (ERSP) showsmean event-related changes in spectral power at each time during the epoch andat each frequency. Intertrial coherence (ITC) indicates degree of that the EEGactivity at a given time and frequency in single trials are phase-locked (notphase-random with respect to the time-locking experimental event). (a) ERSP andITC of component 9 during left-hand movement imagery. (b) ERSP and ITC ofcomponent 9 during right-hand movement imagery. (c) ERSP and ITC of component19 during left-hand movement imagery. (d) ERSP and ITC of component 19 duringright-hand movement imagery.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2267213&req=5

fig9: The event-related spectral perturbation (ERSP) showsmean event-related changes in spectral power at each time during the epoch andat each frequency. Intertrial coherence (ITC) indicates degree of that the EEGactivity at a given time and frequency in single trials are phase-locked (notphase-random with respect to the time-locking experimental event). (a) ERSP andITC of component 9 during left-hand movement imagery. (b) ERSP and ITC ofcomponent 9 during right-hand movement imagery. (c) ERSP and ITC of component19 during left-hand movement imagery. (d) ERSP and ITC of component 19 duringright-hand movement imagery.
Bottom Line: Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features.Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate.The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. qbzhao@sjtu.edu.cn

ABSTRACT
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

No MeSH data available.