Limits...
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.


Channelspectra and associated topographical maps during hand movement imagery.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2267213&req=5

fig1: Channelspectra and associated topographical maps during hand movement imagery.

Mentions: Macroscopic brain activity during resting wakefulnesscontains distinct “idle” rhythms located over various brain areas, forexample, the μ-rhythm can bemeasured over the pericentral sensorimotor cortices in the scalp EEG, usuallywith a frequency of about 10 Hz. Furthermore, there also exists β-rhythm around20 Hz over the human motor cortex. Therefore, 10 Hz μ-rhythm and 20Hz β-rhythm usuallycoexist in noninvasive scalp EEG recordings (see Figure 1).


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

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

Channelspectra and associated topographical maps during hand movement imagery.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Channelspectra and associated topographical maps during hand movement imagery.
Mentions: Macroscopic brain activity during resting wakefulnesscontains distinct “idle” rhythms located over various brain areas, forexample, the μ-rhythm can bemeasured over the pericentral sensorimotor cortices in the scalp EEG, usuallywith a frequency of about 10 Hz. Furthermore, there also exists β-rhythm around20 Hz over the human motor cortex. Therefore, 10 Hz μ-rhythm and 20Hz β-rhythm usuallycoexist in noninvasive scalp EEG recordings (see Figure 1).

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.