@InProceedings{8081415,
  author = {K. Naskovska and A. A. Korobkov and M. Haardt and J. Haueisen},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Analysis of the photic driving effect via joint EEG and MEG data processing based on the coupled CP decomposition},
  year = {2017},
  pages = {1285-1289},
  abstract = {There are many combined signal processing applications such as the joint processing of EEG (Electroencephalogram) and MEG (Magnetoencephalogram) data that can benefit from coupled CP (Canonical Polyadic) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The C-SECSI (Coupled - Semi-Algebraic framework for approximate CP decomposition via Simultaneaous matrix diagonalization) framework provides a semi-algebraic solution for the coupled CP decomposition of noise corrupted low-rank tensors. The C-SECSI framework efficiently computes the factor matrices even in ill-posed scenarios with an adjustable complexity-accuracy trade-off. In this paper, we present a reliability test for the C-SECSI framework that can improve the model order estimation. Moreover, we analyse the photic driving effect from simultaneously recorded EEG and MEG data using the C-SECSI framework. The EEG and MEG data used in the analysis are obtained by stimulating volunteers with flickering light at different frequencies that are multiples of the individual alpha frequency of each volunteer.},
  keywords = {electroencephalography;magnetoencephalography;medical signal processing;neurophysiology;tensors;joint processing;coupled CP tensor decompositions;C-SECSI framework;photic driving effect;combined signal processing applications;MEG data;EEG data;electroencephalogram;magnetoencephalogram;canonical polyadic;coupled-semialgebraic framework;noise corrupted low-rank tensors;ill-posed scenarios;reliability test;complexity-accuracy trade-off;flickering light;alpha frequency;Tensile stress;Electroencephalography;Matrix decomposition;Reliability;Resonant frequency;Time-frequency analysis;Signal processing},
  doi = {10.23919/EUSIPCO.2017.8081415},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344349.pdf},
}

{"_id":"K8quf7JsL5ps5GruR","bibbaseid":"naskovska-korobkov-haardt-haueisen-analysisofthephoticdrivingeffectviajointeegandmegdataprocessingbasedonthecoupledcpdecomposition-2017","authorIDs":[],"author_short":["Naskovska, K.","Korobkov, A. A.","Haardt, M.","Haueisen, J."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["K."],"propositions":[],"lastnames":["Naskovska"],"suffixes":[]},{"firstnames":["A.","A."],"propositions":[],"lastnames":["Korobkov"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Haardt"],"suffixes":[]},{"firstnames":["J."],"propositions":[],"lastnames":["Haueisen"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Analysis of the photic driving effect via joint EEG and MEG data processing based on the coupled CP decomposition","year":"2017","pages":"1285-1289","abstract":"There are many combined signal processing applications such as the joint processing of EEG (Electroencephalogram) and MEG (Magnetoencephalogram) data that can benefit from coupled CP (Canonical Polyadic) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The C-SECSI (Coupled - Semi-Algebraic framework for approximate CP decomposition via Simultaneaous matrix diagonalization) framework provides a semi-algebraic solution for the coupled CP decomposition of noise corrupted low-rank tensors. The C-SECSI framework efficiently computes the factor matrices even in ill-posed scenarios with an adjustable complexity-accuracy trade-off. In this paper, we present a reliability test for the C-SECSI framework that can improve the model order estimation. Moreover, we analyse the photic driving effect from simultaneously recorded EEG and MEG data using the C-SECSI framework. The EEG and MEG data used in the analysis are obtained by stimulating volunteers with flickering light at different frequencies that are multiples of the individual alpha frequency of each volunteer.","keywords":"electroencephalography;magnetoencephalography;medical signal processing;neurophysiology;tensors;joint processing;coupled CP tensor decompositions;C-SECSI framework;photic driving effect;combined signal processing applications;MEG data;EEG data;electroencephalogram;magnetoencephalogram;canonical polyadic;coupled-semialgebraic framework;noise corrupted low-rank tensors;ill-posed scenarios;reliability test;complexity-accuracy trade-off;flickering light;alpha frequency;Tensile stress;Electroencephalography;Matrix decomposition;Reliability;Resonant frequency;Time-frequency analysis;Signal processing","doi":"10.23919/EUSIPCO.2017.8081415","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344349.pdf","bibtex":"@InProceedings{8081415,\n author = {K. Naskovska and A. A. Korobkov and M. Haardt and J. Haueisen},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Analysis of the photic driving effect via joint EEG and MEG data processing based on the coupled CP decomposition},\n year = {2017},\n pages = {1285-1289},\n abstract = {There are many combined signal processing applications such as the joint processing of EEG (Electroencephalogram) and MEG (Magnetoencephalogram) data that can benefit from coupled CP (Canonical Polyadic) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The C-SECSI (Coupled - Semi-Algebraic framework for approximate CP decomposition via Simultaneaous matrix diagonalization) framework provides a semi-algebraic solution for the coupled CP decomposition of noise corrupted low-rank tensors. The C-SECSI framework efficiently computes the factor matrices even in ill-posed scenarios with an adjustable complexity-accuracy trade-off. In this paper, we present a reliability test for the C-SECSI framework that can improve the model order estimation. Moreover, we analyse the photic driving effect from simultaneously recorded EEG and MEG data using the C-SECSI framework. The EEG and MEG data used in the analysis are obtained by stimulating volunteers with flickering light at different frequencies that are multiples of the individual alpha frequency of each volunteer.},\n keywords = {electroencephalography;magnetoencephalography;medical signal processing;neurophysiology;tensors;joint processing;coupled CP tensor decompositions;C-SECSI framework;photic driving effect;combined signal processing applications;MEG data;EEG data;electroencephalogram;magnetoencephalogram;canonical polyadic;coupled-semialgebraic framework;noise corrupted low-rank tensors;ill-posed scenarios;reliability test;complexity-accuracy trade-off;flickering light;alpha frequency;Tensile stress;Electroencephalography;Matrix decomposition;Reliability;Resonant frequency;Time-frequency analysis;Signal processing},\n doi = {10.23919/EUSIPCO.2017.8081415},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344349.pdf},\n}\n\n","author_short":["Naskovska, K.","Korobkov, A. A.","Haardt, M.","Haueisen, J."],"key":"8081415","id":"8081415","bibbaseid":"naskovska-korobkov-haardt-haueisen-analysisofthephoticdrivingeffectviajointeegandmegdataprocessingbasedonthecoupledcpdecomposition-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570344349.pdf"},"keyword":["electroencephalography;magnetoencephalography;medical signal processing;neurophysiology;tensors;joint processing;coupled CP tensor decompositions;C-SECSI framework;photic driving effect;combined signal processing applications;MEG data;EEG data;electroencephalogram;magnetoencephalogram;canonical polyadic;coupled-semialgebraic framework;noise corrupted low-rank tensors;ill-posed scenarios;reliability test;complexity-accuracy trade-off;flickering light;alpha frequency;Tensile stress;Electroencephalography;Matrix decomposition;Reliability;Resonant frequency;Time-frequency analysis;Signal processing"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.655Z","downloads":0,"keywords":["electroencephalography;magnetoencephalography;medical signal processing;neurophysiology;tensors;joint processing;coupled cp tensor decompositions;c-secsi framework;photic driving effect;combined signal processing applications;meg data;eeg data;electroencephalogram;magnetoencephalogram;canonical polyadic;coupled-semialgebraic framework;noise corrupted low-rank tensors;ill-posed scenarios;reliability test;complexity-accuracy trade-off;flickering light;alpha frequency;tensile stress;electroencephalography;matrix decomposition;reliability;resonant frequency;time-frequency analysis;signal processing"],"search_terms":["analysis","photic","driving","effect","via","joint","eeg","meg","data","processing","based","coupled","decomposition","naskovska","korobkov","haardt","haueisen"],"title":"Analysis of the photic driving effect via joint EEG and MEG data processing based on the coupled CP decomposition","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}