Visibility Graphs for Time Series Containing Different Components

Qi, Jingchao; Wang, Jianyong; Wang, Jianbo; Xiao, Qin; Yang, Huijie

doi:10.1142/s0219477511000636

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…The multicluster feature selection (MCFS) algorithm was applied to sort features [30]. The basic principle of MCFS is first to construct a p -nearest neighbor graph according to (21):…”

Section: Feature Selection and Classificationmentioning

confidence: 99%

“…EEG signals are recorded as time-series signals of brain activity, and studies show that such time-series signals captured at different brain regions reflect the brain activity synergy of their corresponding brain regions. Such EEG time-series signals acquired from multiple locations of the brain form a brain network [21,22], and cognitive activities can be analyzed by extracting different measures in the brain network in order to reflect differences between brain regions activated by different users. Finally, the classification accuracy can be improved.…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction of four-class motor imagery EEG signals based on functional brain network

Chen

et al. 2019

J. Neural Eng.

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Objective. A motor-imagery-based brain-computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach. This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results. As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance. The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.

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“…The multicluster feature selection (MCFS) algorithm was applied to sort features [30]. The basic principle of MCFS is first to construct a p -nearest neighbor graph according to (21):…”

Section: Feature Selection and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature extraction of four-class motor imagery EEG signals based on functional brain network

Chen

et al. 2019

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…We have done some work on the mixture problem for a one-dimensional visibility graph. [70] To understand the results for real-world landscapes, detailed and systematic works based upon modelgenerated landscapes are required to serve as benchmarks.…”

Section: Discussionmentioning

confidence: 99%