The Experts below are selected from a list of 258111 Experts worldwide ranked by ideXlab platform
Cuntai Guan - One of the best experts on this subject based on the ideXlab platform.
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filter bank common Spatial Pattern algorithm on bci competition iv datasets 2a and 2b
Frontiers in Neuroscience, 2012Co-Authors: Kai Keng Ang, Cuntai Guan, Zheng Yang Chin, Chuanchu Wang, Haihong ZhangAbstract:The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
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Spatially sparsed common Spatial Pattern to improve bci performance
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Mahnaz Arvaneh, Kai Keng Ang, Cuntai Guan, Hiok Chai QuekAbstract:Common Spatial Pattern (CSP) is widely used in discriminating two classes of EEG in Brain Computer Interface applications. However, the performance of the CSP algorithm is affected by noise and artifacts, and the problem is more pronounced in small training data. To overcome these drawbacks, this paper proposes a new Spatially Sparsed CSP (SS-CSP) algorithm by inducing sparsity in the Spatial filters. The proposed algorithm optimizes the Spatial filters to emphasize the regions that have high variances between classes, and attenuates the regions with low or irregular variances which can be due to noise or artifacts. The experimental results on 14 subjects from publicly available BCI competition datasets showed that the proposed SSCSP algorithm significantly improved the performance of the subjects with poor CSP accuracy by an average of 11%. The results also showed that the obtained sparse Spatial filters are more neurophysilogically relevant.
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multiclass voluntary facial expression classification based on filter bank common Spatial Pattern
International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Zheng Yang Chin, Kai Keng Ang, Cuntai GuanAbstract:This paper investigates the classification of voluntary facial expressions from electroencephalogram (EEG) and electromyogram (EMG) signals using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm is an autonomous and effective machine learning approach for classifying two classes of EEG measurements in motor imagery-based Brain Computer Interface (BCI). However, the problem of facial expression recognition typically involves more than just two classes of measurements. Hence, this paper proposes an extension of FBCSP to the multiclass paradigm using a decision threshold-based classifier for classifying facial expressions from EEG and EMG measurements. A study is conducted using the proposed Multiclass FBCSP on 4 subjects who performed 6 different facial expressions. The results show that the Multiclass FBCSP is effective in classifying multiple facial expressions from the EEG and EMG measurements.
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filter bank common Spatial Pattern fbcsp in brain computer interface
International Joint Conference on Neural Network, 2008Co-Authors: Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai GuanAbstract:In motor imagery-based brain computer interfaces (BCI), discriminative Patterns can be extracted from the electroencephalogram (EEG) using the common Spatial Pattern (CSP) algorithm. However, the performance of this Spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-Spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
Kai Keng Ang - One of the best experts on this subject based on the ideXlab platform.
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filter bank common Spatial Pattern algorithm on bci competition iv datasets 2a and 2b
Frontiers in Neuroscience, 2012Co-Authors: Kai Keng Ang, Cuntai Guan, Zheng Yang Chin, Chuanchu Wang, Haihong ZhangAbstract:The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
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Spatially sparsed common Spatial Pattern to improve bci performance
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Mahnaz Arvaneh, Kai Keng Ang, Cuntai Guan, Hiok Chai QuekAbstract:Common Spatial Pattern (CSP) is widely used in discriminating two classes of EEG in Brain Computer Interface applications. However, the performance of the CSP algorithm is affected by noise and artifacts, and the problem is more pronounced in small training data. To overcome these drawbacks, this paper proposes a new Spatially Sparsed CSP (SS-CSP) algorithm by inducing sparsity in the Spatial filters. The proposed algorithm optimizes the Spatial filters to emphasize the regions that have high variances between classes, and attenuates the regions with low or irregular variances which can be due to noise or artifacts. The experimental results on 14 subjects from publicly available BCI competition datasets showed that the proposed SSCSP algorithm significantly improved the performance of the subjects with poor CSP accuracy by an average of 11%. The results also showed that the obtained sparse Spatial filters are more neurophysilogically relevant.
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multiclass voluntary facial expression classification based on filter bank common Spatial Pattern
International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Zheng Yang Chin, Kai Keng Ang, Cuntai GuanAbstract:This paper investigates the classification of voluntary facial expressions from electroencephalogram (EEG) and electromyogram (EMG) signals using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm is an autonomous and effective machine learning approach for classifying two classes of EEG measurements in motor imagery-based Brain Computer Interface (BCI). However, the problem of facial expression recognition typically involves more than just two classes of measurements. Hence, this paper proposes an extension of FBCSP to the multiclass paradigm using a decision threshold-based classifier for classifying facial expressions from EEG and EMG measurements. A study is conducted using the proposed Multiclass FBCSP on 4 subjects who performed 6 different facial expressions. The results show that the Multiclass FBCSP is effective in classifying multiple facial expressions from the EEG and EMG measurements.
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filter bank common Spatial Pattern fbcsp in brain computer interface
International Joint Conference on Neural Network, 2008Co-Authors: Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai GuanAbstract:In motor imagery-based brain computer interfaces (BCI), discriminative Patterns can be extracted from the electroencephalogram (EEG) using the common Spatial Pattern (CSP) algorithm. However, the performance of this Spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-Spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
Ruihua Sun - One of the best experts on this subject based on the ideXlab platform.
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a sparse common Spatial Pattern algorithm for brain computer interface
International Conference on Neural Information Processing, 2011Co-Authors: Lichen Shi, Ruihua SunAbstract:Common Spatial Pattern (CSP) algorithm and principal component analysis (PCA) are two commonly used key techniques for EEG component selection and EEG feature extraction for EEG-based brain-computer interfaces (BCIs). However, both the ordinary CSP and PCA algorithms face a loading problem, i.e., their weights in linear combinations are non-zero. This problem makes a BCI system easy to be over-fitted during training process, because not all of the information from EEG data are relevant to the given tasks. To deal with the loading problem, this paper proposes a spare CSP algorithm and introduces a sparse PCA algorithm to BCIs. The performance of BCIs using the proposed sparse CSP and sparse PCA techniques is evaluated on a motor imagery classification task and a vigilance estimation task. Experimental results demonstrate that the BCI system with sparse PCA and sparse CSP techniques are superior to that using the ordinary PCA and CSP algorithms.
Akos T Kovacs - One of the best experts on this subject based on the ideXlab platform.
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density of founder cells affects Spatial Pattern formation and cooperation in bacillus subtilis biofilms
The ISME Journal, 2014Co-Authors: Jordi Van Gestel, Franz J Weissing, Oscar P Kuipers, Akos T KovacsAbstract:In nature, most bacteria live in surface-attached sedentary communities known as biofilms. Biofilms are often studied with respect to bacterial interactions. Many cells inhabiting biofilms are assumed to express 'cooperative traits', like the secretion of extracellular polysaccharides (EPS). These traits can enhance biofilm-related properties, such as stress resilience or colony expansion, while being costly to the cells that express them. In well-mixed populations cooperation is difficult to achieve, because non-cooperative individuals can reap the benefits of cooperation without having to pay the costs. The physical process of biofilm growth can, however, result in the Spatial segregation of cooperative from non-cooperative individuals. This segregation can prevent non-cooperative cells from exploiting cooperative neighbors. Here we examine the interaction between Spatial Pattern formation and cooperation in Bacillus subtilis biofilms. We show, experimentally and by mathematical modeling, that the density of cells at the onset of biofilm growth affects Pattern formation during biofilm growth. At low initial cell densities, co-cultured strains strongly segregate in space, whereas Spatial segregation does not occur at high initial cell densities. As a consequence, EPS-producing cells have a competitive advantage over non-cooperative mutants when biofilms are initiated at a low density of founder cells, whereas EPS-deficient cells have an advantage at high cell densities. These results underline the importance of Spatial Pattern formation for competition among bacterial strains and the evolution of microbial cooperation.
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density of founder cells affects Spatial Pattern formation and cooperation in bacillus subtilis biofilms
The ISME Journal, 2014Co-Authors: Jordi Van Gestel, Franz J Weissing, Oscar P Kuipers, Akos T KovacsAbstract:In nature, most bacteria live in surface-attached sedentary communities known as biofilms. Biofilms are often studied with respect to bacterial interactions. Many cells inhabiting biofilms are assumed to express ‘cooperative traits’, like the secretion of extracellular polysaccharides (EPS). These traits can enhance biofilm-related properties, such as stress resilience or colony expansion, while being costly to the cells that express them. In well-mixed populations cooperation is difficult to achieve, because non-cooperative individuals can reap the benefits of cooperation without having to pay the costs. The physical process of biofilm growth can, however, result in the Spatial segregation of cooperative from non-cooperative individuals. This segregation can prevent non-cooperative cells from exploiting cooperative neighbors. Here we examine the interaction between Spatial Pattern formation and cooperation in Bacillus subtilis biofilms. We show, experimentally and by mathematical modeling, that the density of cells at the onset of biofilm growth affects Pattern formation during biofilm growth. At low initial cell densities, co-cultured strains strongly segregate in space, whereas Spatial segregation does not occur at high initial cell densities. As a consequence, EPS-producing cells have a competitive advantage over non-cooperative mutants when biofilms are initiated at a low density of founder cells, whereas EPS-deficient cells have an advantage at high cell densities. These results underline the importance of Spatial Pattern formation for competition among bacterial strains and the evolution of microbial cooperation. The ISME Journal (2014) 8, 2069–2079; doi:10.1038/ismej.2014.52; published online 3 April 2014 Subject Category: Evolutionary genetics
Juan C Corley - One of the best experts on this subject based on the ideXlab platform.
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RESEARCH ARTICLE Spatial Pattern of Attacks of the Invasive Woodwasp Sirex noctilio, at Landscape and Stand
2016Co-Authors: Victoria M Lantschner, Juan C CorleyAbstract:Invasive insect pests are responsible for important damage to native and plantation forests, when population outbreaks occur. Understanding the Spatial Pattern of attacks by forest pest populations is essential to improve our understanding of insect population dynamics and for predicting attack risk by invasives or planning pest management strategies. The woodwasp Sirex noctilio is an invasive woodwasp that has become probably the most im-portant pest of pine plantations in the Southern Hemisphere. Our aim was to study the spa-tial dynamics of S. noctilio populations in Southern Argentina. Specifically we describe: (1) the Spatial Patterns of S. noctilio outbreaks and their relation with environmental factors at a landscape scale; and (2) characterize the Spatial Pattern of attacked trees at the stand scale. We surveyed the Spatial distribution of S. noctilio outbreaks in three pine plantation landscapes, and we assessed potential associations with topographic variables, habitat characteristics, and distance to other outbreaks. We also looked at the Spatial distribution of attacked trees in 20 stands with different levels of infestation, and assessed the relationship of attacks with stand composition and management. We found that the Spatial Pattern of pine stands with S. noctilio outbreaks at the landscape scale is influenced mainly by the host species present, slope aspect, and distance to other outbreaks. At a stand scale, there is strong aggregation of attacked trees in stands with intermediate infestation levels, and the degree of attacks is influenced by host species and plantation management. We con-clude that the Pattern of S. noctilio damage at different Spatial scales is influenced by a com-bination of both inherent population dynamics and the underlying Patterns of environmental factors. Our results have important implications for the understanding and management of invasive insect outbreaks in forest systems
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Spatial Pattern of attacks of the invasive woodwasp sirex noctilio at landscape and stand scales
PLOS ONE, 2015Co-Authors: Victoria M Lantschner, Juan C CorleyAbstract:Invasive insect pests are responsible for important damage to native and plantation forests, when population outbreaks occur. Understanding the Spatial Pattern of attacks by forest pest populations is essential to improve our understanding of insect population dynamics and for predicting attack risk by invasives or planning pest management strategies. The woodwasp Sirex noctilio is an invasive woodwasp that has become probably the most important pest of pine plantations in the Southern Hemisphere. Our aim was to study the Spatial dynamics of S. noctilio populations in Southern Argentina. Specifically we describe: (1) the Spatial Patterns of S. noctilio outbreaks and their relation with environmental factors at a landscape scale; and (2) characterize the Spatial Pattern of attacked trees at the stand scale. We surveyed the Spatial distribution of S. noctilio outbreaks in three pine plantation landscapes, and we assessed potential associations with topographic variables, habitat characteristics, and distance to other outbreaks. We also looked at the Spatial distribution of attacked trees in 20 stands with different levels of infestation, and assessed the relationship of attacks with stand composition and management. We found that the Spatial Pattern of pine stands with S. noctilio outbreaks at the landscape scale is influenced mainly by the host species present, slope aspect, and distance to other outbreaks. At a stand scale, there is strong aggregation of attacked trees in stands with intermediate infestation levels, and the degree of attacks is influenced by host species and plantation management. We conclude that the Pattern of S. noctilio damage at different Spatial scales is influenced by a combination of both inherent population dynamics and the underlying Patterns of environmental factors. Our results have important implications for the understanding and management of invasive insect outbreaks in forest systems.