Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization

Kamijo, Keita; Kiyuna, Tomoharu; Takaki, Yoko; Kenmochi, Akihisa; Tanigawa, Tetsuji; Yamazaki, Toshimasa

doi:10.1163/156855700750265468

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…ECDL was applied to the reconstructed EEGs, namely the projection of each of the rest ICs on the scalp surface by the deflation procedure, using "SynaCenterPro" (PC-based commercial software for multiple ECDL) (NEC Corporation). This software estimates unconstrained dipoles [19] at any timepoint, using the three-layered concentric sphere head model by the nonlinear optimization methods [20]. An unconstrained dipole was estimated at any timepoint with maximal peak or trough in the reconstructed EEGs for each IC.…”

Section: Ecdlmentioning

confidence: 99%

Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

Sakamoto

Yamaguchi

Yamazaki

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYWe have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the singletrial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

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Section: Ecdlmentioning

confidence: 99%

Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

Sakamoto

Yamaguchi

Yamazaki

et al. 2015

IEICE Trans. Inf. & Syst.

Self Cite

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“…Many methods have been proposed to minimize the cost function J , such as the Levenberg-Marquardt method, the downhill simplex method, the Powell method, the simulated annealing method and the genetic method (He et al, 1987;He & Musha, 1992;Khosla et al, 1997;Yamazaki et al, 2000;Kamijo et al, 2001). In the present study, the Powell algorithm is selected to minimize the cost function J because of its good performance in the small-residual case.…”

Section: Dipole Source Localizationmentioning

confidence: 99%

“…Recently, Kamijo et al (2001) used a neural network method to identify the number of ECDs based on ISM. At its heart is a multilayer neural network which takes the measured scalp potential distribution as inputs, uses one hidden layer, and generates one output -the number of ECDs.…”

Section: Introductionmentioning

confidence: 99%

On the estimation of the number of dipole sources in EEG source localization

Xiang¹,

He²

2005

Clinical Neurophysiology

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Background-The purpose of the present study was to determine the number of the equivalent dipole sources corresponding to the scalp EEG using the information criterion method based on the instantaneous-state modeling.Methods-A three-concentric-spheres head model was used to represent the head volume conductor. The Powell algorithm was used to solve the inverse problem of estimating the equivalent dipoles from the scalp EEG. The information criterion with different penalty functions was used to determine the dipole number. Computer simulations were conducted to evaluate effects of various parameters on the estimation of dipole number.Results-The present results suggest that the present method is able to estimate the number of equivalent current dipoles (ECDs) from instantaneous scalp EEG measurements, and that increase in the electrode number can improve the accuracy of estimation of the ECD number. For two ECDs, the best performance of estimation with 20% white noise were 85%, 92% and 94%, when 64, 128 and 256 electrodes are used, respectively. When there are 3 ECDs, the present results suggest that using 256 electrodes gave up to 82% estimation accuracy. The present simulation results also indicate that the accuracies of identification are similar when the minimum distance between dipoles is either 1 or 2 cm, which was used in the simulation. It was also found that the different penalty functions used in the information criterion method could have substantial influence on the estimation accuracy.Conclusions-The present method can estimate the number of ECDs from instantaneous scalp EEG distribution for up to three dipoles.Significance-The successful estimation of the number of ECDs will play an important role in expanding the applicability of dipole source localization to multiple sources.

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“…[Sun and Sclabassi, 2000] adapted an MLP to speed up the calculation of forward EEG solutions for a spheroidal head model from simple EEG solutions for a spherical head model. Recently, [Kamijo et al, 2001] and [Jun et al, 2002] studied hybrid approaches to EEG/MEG dipole source localization, in which trained MLPs are used as initializers for iterative methods. In addition, [Jun et al, 2003] proposed an MLPbased MEG dipole source localizer that uses a distributed output representation in the MLP structure, which is expected to be more easily extensible to the multiple dipole case.…”

Section: Introductionmentioning

confidence: 99%

Fast robust subject‐independent magnetoencephalographic source localization using an artificial neural network

Jun

Pearlmutter

2004

Human Brain Mapping

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Abstract:We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software. Hum Brain Mapp 24:21-34, 2005.

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Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization

Cited by 12 publications

References 17 publications

Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

On the estimation of the number of dipole sources in EEG source localization

Fast robust subject‐independent magnetoencephalographic source localization using an artificial neural network

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