Alfonso de Hoyos1, Javier Portillo2, Pilar Marín3, F Maestú4, J Lafuente M5, Antonio Hernando6. 1. Instituto de Magnetismo Aplicado, Universidad Complutense de Madrid, ADIF, CSIC, Madrid, Spain. Electronic address: adehoyos@externos.adif.es. 2. Grupo de Procesado de Datos y Simulación, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain. Electronic address: javierp@grpss.ssr.upm.es. 3. Instituto de Magnetismo Aplicado, Universidad Complutense de Madrid, ADIF, CSIC, Madrid, Spain; Departamento de Física de Materiales, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain. Electronic address: pmarin@externos.adif.es. 4. Laboratorio de Neurociencia Cognitiva y Computacional, UCM-UPM. Centro de Tecnología Biomédica, Spain; Departamento de Psicología Básica II, Facultad de Psicología, Universidad Complutense de Madrid, Madrid, Spain. Electronic address: fmaestuu@psi.ucm.es. 5. Departamento de Radiodiagnóstico del Hospital General Universitario Gregorio Marañón, Madrid, Spain. Electronic address: javier.lafuente@telefonica.net. 6. Instituto de Magnetismo Aplicado, Universidad Complutense de Madrid, ADIF, CSIC, Madrid, Spain; Departamento de Física de Materiales, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain. Electronic address: antonio.hernando@externos.adif.es.
Abstract
BACKGROUND: Quite often, magnetoencephalography (MEG) measurements are contaminated by a series of artifacts that degrade the quality of the various source localization methods applied to them. In particular, eye blinking, minor head movement and related activities are a constant source of measurement contamination. In order to solve this problem, trial selection and rejection is applied, a task that is usually performed manually. NEW METHOD: The present work shows an automatic trial selection and rejection algorithm based on clustering techniques. These techniques employ a measurement of the dissimilarity of the items belonging to a set. This measure, based on the projection of the eigenvector corresponding to the largest eigenvalue of the covariance matrix, is provided and its rationale is explained. Subsequently, covariance matrices belonging to the selected cluster are averaged and used in the well-known Linearly Constrained Minimum Variance (LCMV) Beamformer. RESULTS: The results show a marked improvement of the specificity of the localization algorithm compared to the application of the LCMV without clustering. COMPARISON WITH EXISTING METHOD(S): The method shows a marked reduction in computational cost compared with other data cleaning procedure widely used: Independent Component Analysis (ICA). CONCLUSIONS: Thus, we propose clustering techniques to be used in brain localization activity algorithms.
BACKGROUND: Quite often, magnetoencephalography (MEG) measurements are contaminated by a series of artifacts that degrade the quality of the various source localization methods applied to them. In particular, eye blinking, minor head movement and related activities are a constant source of measurement contamination. In order to solve this problem, trial selection and rejection is applied, a task that is usually performed manually. NEW METHOD: The present work shows an automatic trial selection and rejection algorithm based on clustering techniques. These techniques employ a measurement of the dissimilarity of the items belonging to a set. This measure, based on the projection of the eigenvector corresponding to the largest eigenvalue of the covariance matrix, is provided and its rationale is explained. Subsequently, covariance matrices belonging to the selected cluster are averaged and used in the well-known Linearly Constrained Minimum Variance (LCMV) Beamformer. RESULTS: The results show a marked improvement of the specificity of the localization algorithm compared to the application of the LCMV without clustering. COMPARISON WITH EXISTING METHOD(S): The method shows a marked reduction in computational cost compared with other data cleaning procedure widely used: Independent Component Analysis (ICA). CONCLUSIONS: Thus, we propose clustering techniques to be used in brain localization activity algorithms.
Authors: Lily Riggs; Takako Fujioka; Jessica Chan; Douglas A McQuiggan; Adam K Anderson; Jennifer D Ryan Journal: Front Hum Neurosci Date: 2014-12-18 Impact factor: 3.169