@InProceedings{8553028,
  author = {G. Safont and A. Salazar and J. Belda and L. Vergara},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Semi-supervised Learning for Dynamic Modeling of Brain Signals During Visual and Auditory Tests},
  year = {2018},
  pages = {1192-1196},
  abstract = {Requirements of costly data labeling for data classification are relaxed with semi-supervised learning. This is particularly useful considering monitoring of a physiological process that continuously produces data and can be observed for a long time. We propose a new expectation-maximization (EM) procedure that implements semi-supervised learning and it is based on sequential independent component analysis modeling (SICAMM), that we have called EM-SICAMM. This procedure is applied for dynamic modeling of EEG signals measured from epileptic patients during visual and auditory neuropsychological tests. Those tests are done to evaluate the learning and memory cognitive function of the patients. Classification results demonstrate that EM-SICAMM outperforms, in terms of balanced error rate (BER) and kappa index, the following competitive methods: ICAMM, SICAMM, Gaussian mixture model (GMM), and hidden Markov model (HMM).},
  keywords = {cognition;electroencephalography;Gaussian processes;hearing;hidden Markov models;independent component analysis;learning (artificial intelligence);medical disorders;medical signal processing;mixture models;neurophysiology;signal classification;vision;semisupervised learning;dynamic modeling;brain signals;data classification;expectation-maximization procedure;sequential independent component analysis modeling;EM-SICAMM;visual tests;auditory neuropsychological tests;memory cognitive function;physiological process;EEG signals;epileptic patients;balanced error rate;kappa index;Gaussian mixture model;hidden Markov model;Hidden Markov models;Brain modeling;Electroencephalography;Semisupervised learning;Visualization;Independent component analysis;Signal processing;semi-supervised learning;dynamic modeling;ICA;EEG;neuropsychological tests},
  doi = {10.23919/EUSIPCO.2018.8553028},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437820.pdf},
}

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This is particularly useful considering monitoring of a physiological process that continuously produces data and can be observed for a long time. We propose a new expectation-maximization (EM) procedure that implements semi-supervised learning and it is based on sequential independent component analysis modeling (SICAMM), that we have called EM-SICAMM. This procedure is applied for dynamic modeling of EEG signals measured from epileptic patients during visual and auditory neuropsychological tests. Those tests are done to evaluate the learning and memory cognitive function of the patients. Classification results demonstrate that EM-SICAMM outperforms, in terms of balanced error rate (BER) and kappa index, the following competitive methods: ICAMM, SICAMM, Gaussian mixture model (GMM), and hidden Markov model (HMM).","keywords":"cognition;electroencephalography;Gaussian processes;hearing;hidden Markov models;independent component analysis;learning (artificial intelligence);medical disorders;medical signal processing;mixture models;neurophysiology;signal classification;vision;semisupervised learning;dynamic modeling;brain signals;data classification;expectation-maximization procedure;sequential independent component analysis modeling;EM-SICAMM;visual tests;auditory neuropsychological tests;memory cognitive function;physiological process;EEG signals;epileptic patients;balanced error rate;kappa index;Gaussian mixture model;hidden Markov model;Hidden Markov models;Brain modeling;Electroencephalography;Semisupervised learning;Visualization;Independent component analysis;Signal processing;semi-supervised learning;dynamic modeling;ICA;EEG;neuropsychological tests","doi":"10.23919/EUSIPCO.2018.8553028","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437820.pdf","bibtex":"@InProceedings{8553028,\n author = {G. Safont and A. Salazar and J. Belda and L. Vergara},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Semi-supervised Learning for Dynamic Modeling of Brain Signals During Visual and Auditory Tests},\n year = {2018},\n pages = {1192-1196},\n abstract = {Requirements of costly data labeling for data classification are relaxed with semi-supervised learning. This is particularly useful considering monitoring of a physiological process that continuously produces data and can be observed for a long time. We propose a new expectation-maximization (EM) procedure that implements semi-supervised learning and it is based on sequential independent component analysis modeling (SICAMM), that we have called EM-SICAMM. This procedure is applied for dynamic modeling of EEG signals measured from epileptic patients during visual and auditory neuropsychological tests. Those tests are done to evaluate the learning and memory cognitive function of the patients. 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