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20123
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Subject: source localization

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Now showing 1 - 3 of 3
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    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    An Extended Kalman Filtering Approach for the Estimation of Human Head Tissue Conductivities by Using Eeg Data: a Simulation Study
    (Iop Publishing Ltd, 2012) Sengul, G.; Baysal, U.
    In this study, we propose an extended Kalman filter approach for the estimation of the human head tissue conductivities in vivo by using electroencephalogram (EEG) data. Since the relationship between the surface potentials and conductivity distribution is nonlinear, the proposed algorithm first linearizes the system and applies extended Kalman filtering. By using a three-compartment realistic head model obtained from the magnetic resonance images of a real subject, a known dipole assumption and 32 electrode positions, the performance of the proposed method is tested in simulation studies and it is shown that the proposed algorithm estimates the tissue conductivities with less than 1% error in noiseless measurements and less than 5% error when the signal-to-noise ratio is 40 dB or higher. We conclude that the proposed extended Kalman filter approach successfully estimates the tissue conductivities in vivo.
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    Article
    Determination of Measurement Noise, Conductivity Errors and Electrode Mislocalization Effects To Somatosensory Dipole Localization.
    (Allied Acad, 2012) Sengul, G.; Baysal, U.; Computer Engineering
    Calculating the spatial locations, directions and magnitudes of electrically active sources of human brain by using the measured scalp potentials is known as source localization. An accurate source localization method requires not only EEG data but also the 3-D positions and number of measurement electrodes, the numerical head model of the patient/subject and the conductivities of the layers used in the head model. In this study we computationally determined the effect of noise, conductivity errors and electrode mislocalizations for electrical sources located in somatosensory cortex. We first randomly selected 1000 electric sources in somatosensory cortex, and for these sources we simulated the surface potentials by using average conductivities given in the literature and 3-D positions of the electrodes. We then added random noise to measurements and by using noisy data; we tried to calculate the positions of the dipoles by using different electrode positions or different conductivity values. The estimated electrical sources and original ones are compared and by this way the effect of measurement noise, electrode mislocalizations and conductivity errors to somatosensory dipole localization is investigated. We conclude that for an accurate somatosensory source localization method, we need noiseless measurements, accurate conductivity values of scalp and skull layers and the accurate knowledge of 3-D positions of measurement sensors.
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    Article
    The Effect of Statistically Constrained Minimum Mean Square Estimation Algorithm Which Is Used for Human Head Tissue Conductivity Estimation To Source Localization
    (Journal Neurological Sciences, 2012) Sengul, Gokhan; Şengül, Gökhan; Baysal, Ugur; Şengül, Gökhan; Computer Engineering; Computer Engineering; Computer Engineering
    Determining the electrical active regions of human brain by using EEG and/or MEG data is known as "EEG/MEG bioelectromagnetic inverse problem" or "source localization". A typical source localization system intakes not only EEG/MEG data but also geometry information of subject/patient, a priori information about the electrically active sources, the number and 3-D positions of measurement electrodes and conductivities/resistivities of the tissues in the head model. In this study we investigated the conductivity estimation performance previously proposed Statistically Constrainted Minimum Mean Square Error Estimation (MiMSEE) algorithm by simulation studies and we also investigated the effect of the estimation to source localization activities. In simulation studies we used a three-layered (composed of scalp, skull and brain regions) realistic head model to estimate 100 different conductivity distributions in vivo. As a result we found that the proposed algorithm estimates the conductivity of scalp with an average error of 23%, the conductivity of skull with an average error of 40% and finally the conductivity of brain with an average error of 17%. In the second part of the study we compared the source localization errors for two cases: one, when the average conductivities of tissues given in the literature are used, and second when the subject-specific conductivity estimation is performed with MiMSEE algorithm. The results showed 10.1 mm localization error is obtained when the average conductivities given in the literature are used and 2.7 mm localization is obtained when subject-specific conductivity estimation is performed with MiMSEE algorithm. The results shows that the localization error is reduced by 73.07% when subject-specific conductivity estimation is performed with MiMSEE algorithm. We conclude that using the conductivities obtained from MiMSEE algorithm reduces the source localization error and we recommend to perform subject-specific conductivity estimation for source localization applications.