The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform
G.f. Inbar - One of the best experts on this subject based on the ideXlab platform.
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Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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Unsupervised identification of event-related brain potentials via competitive learning
Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards, 1998Co-Authors: D.h. Lange, G.f. Inbar, H. Pratt, H.t. SiegelmannAbstract:We present a novel approach to the problem of Event-Related Potential (ERP) identification, based on a competitive Artificial Neural Net (ANN). Our approach dismisses the need for stimulus- or event-related selective averaging, thus avoiding conventional assumptions on response invariability. The identifier is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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NIPS - A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure
1997Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive Neural Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions and compared to the expected performance from an information theoretic perspective. The classifier is applied to real event-related potential data recorded during a classic odd-ball type paradigm; for the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.
D.h. Lange - One of the best experts on this subject based on the ideXlab platform.
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Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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Unsupervised identification of event-related brain potentials via competitive learning
Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards, 1998Co-Authors: D.h. Lange, G.f. Inbar, H. Pratt, H.t. SiegelmannAbstract:We present a novel approach to the problem of Event-Related Potential (ERP) identification, based on a competitive Artificial Neural Net (ANN). Our approach dismisses the need for stimulus- or event-related selective averaging, thus avoiding conventional assumptions on response invariability. The identifier is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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NIPS - A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure
1997Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive Neural Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions and compared to the expected performance from an information theoretic perspective. The classifier is applied to real event-related potential data recorded during a classic odd-ball type paradigm; for the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.
H.t. Siegelmann - One of the best experts on this subject based on the ideXlab platform.
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Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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Unsupervised identification of event-related brain potentials via competitive learning
Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards, 1998Co-Authors: D.h. Lange, G.f. Inbar, H. Pratt, H.t. SiegelmannAbstract:We present a novel approach to the problem of Event-Related Potential (ERP) identification, based on a competitive Artificial Neural Net (ANN). Our approach dismisses the need for stimulus- or event-related selective averaging, thus avoiding conventional assumptions on response invariability. The identifier is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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NIPS - A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure
1997Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive Neural Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions and compared to the expected performance from an information theoretic perspective. The classifier is applied to real event-related potential data recorded during a classic odd-ball type paradigm; for the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.
H. Pratt - One of the best experts on this subject based on the ideXlab platform.
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Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
IEEE Transactions on Biomedical Engineering, 2000Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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Unsupervised identification of event-related brain potentials via competitive learning
Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards, 1998Co-Authors: D.h. Lange, G.f. Inbar, H. Pratt, H.t. SiegelmannAbstract:We present a novel approach to the problem of Event-Related Potential (ERP) identification, based on a competitive Artificial Neural Net (ANN). Our approach dismisses the need for stimulus- or event-related selective averaging, thus avoiding conventional assumptions on response invariability. The identifier is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-Session Variable signal patterns are automatically identified dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.
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NIPS - A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure
1997Co-Authors: D.h. Lange, H. Pratt, H.t. Siegelmann, G.f. InbarAbstract:We present a novel generic approach to the problem of Event Related Potential identification and classification, based on a competitive Neural Net architecture. The network weights converge to the embedded signal patterns, resulting in the formation of a matched filter bank. The network performance is analyzed via a simulation study, exploring identification robustness under low SNR conditions and compared to the expected performance from an information theoretic perspective. The classifier is applied to real event-related potential data recorded during a classic odd-ball type paradigm; for the first time, within-Session Variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a-priori stimulus-related selective grouping of the recorded data.
Liu Wenjun - One of the best experts on this subject based on the ideXlab platform.
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IITA - Amelioration Design of Customer-Behavior Analyzing Engine
Workshop on Intelligent Information Technology Application (IITA 2007), 2007Co-Authors: Liu WenjunAbstract:This article proposes an amelioration design of Customer-Behavior analyzing engine. This engine is constructed on clicks stream analyzing method which expands ExLF log file formats and distinguishes between users with Cookie recognition mechanism and embedded Session Variable. This method also distinguishes between user-visit affairs by the time window model, and presents detailed database tables from the data source. Through clustering and CLV value analysis, it can recognize core customers. And then, the article combines clicks-stream dialog files with commercial website's inner data, and mines multidimensional associative rules by improved Apriori algorithm in order to analyze customer behavior and interests models.
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Amelioration Design of Customer-Behavior Analyzing Engine
Workshop on Intelligent Information Technology Application (IITA 2007), 2007Co-Authors: Liu WenjunAbstract:This article proposes an amelioration design of Customer-Behavior analyzing engine. This engine is constructed on clicks stream analyzing method which expands ExLF log file formats and distinguishes between users with Cookie recognition mechanism and embedded Session Variable. This method also distinguishes between user-visit affairs by the time window model, and presents detailed database tables from the data source. Through clustering and CLV value analysis, it can recognize core customers. And then, the article combines clicks-stream dialog files with commercial website's inner data, and mines multidimensional associative rules by improved Apriori algorithm in order to analyze customer behavior and interests models.