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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: 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.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 scalp mapprojection of the IRA components in 3D head model. Components 9 and 19 werehighly related to the motor imagery task, while components 1 and 2 wereassociated with the occipital alpha rhythm.
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fig7: The scalp mapprojection of the IRA components in 3D head model. Components 9 and 19 werehighly related to the motor imagery task, while components 1 and 2 wereassociated with the occipital alpha rhythm.

Mentions: According to IRA algorithm, the components aremutually independent, each column in the mixing matrix, represents a spatialmap describing the relative projection weights of the corresponding temporalcomponents at each EEG channel. These spatial maps will hereinafter be referredto as IC spatial map. Figure 7 shows 30 IC spatial maps for 30 temporalindependent components. In IRA maps, IC9 and IC19 mainly cover left and rightmotor field of brain which are highly related to the motor imagery task.Therefore, these components can be regarded as source signals that are mosteffective for classification, which are testified further by mutual informationin the Section 4.4.


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

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

The scalp mapprojection of the IRA components in 3D head model. Components 9 and 19 werehighly related to the motor imagery task, while components 1 and 2 wereassociated with the occipital alpha rhythm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: The scalp mapprojection of the IRA components in 3D head model. Components 9 and 19 werehighly related to the motor imagery task, while components 1 and 2 wereassociated with the occipital alpha rhythm.
Mentions: According to IRA algorithm, the components aremutually independent, each column in the mixing matrix, represents a spatialmap describing the relative projection weights of the corresponding temporalcomponents at each EEG channel. These spatial maps will hereinafter be referredto as IC spatial map. Figure 7 shows 30 IC spatial maps for 30 temporalindependent components. In IRA maps, IC9 and IC19 mainly cover left and rightmotor field of brain which are highly related to the motor imagery task.Therefore, these components can be regarded as source signals that are mosteffective for classification, which are testified further by mutual informationin the Section 4.4.

Bottom Line: 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.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.