EEG low-resolution brain electromagnetic tomography (LORETA) in Huntington’s disease

Painold, Annamaria; Anderer, Peter; Holl, Anna K.; Letmaier, Martin; Saletu-Zyhlarz, Gerda; Saletu, B.; Bonelli, Raphael M.

doi:10.1007/s00415-010-5852-5

Cited by 31 publications

(40 citation statements)

References 80 publications

(138 reference statements)

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“…The results of EEG studies in HD and in animal models of HD imply the relevance of quantitative EEG characteristics as potential biomarkers of HD [22]. EEG abnormalities in HD patients consist of a marked reduction in alpha power and an increase in relative delta and beta power [23][24][25][26][27]. However, Hunter et al, (2010) found a trend toward higher global absolute alpha power in HD patients when compared to controls [28].…”

Section: Introductionmentioning

confidence: 98%

Alpha–theta border EEG abnormalities in preclinical Huntington's disease

Ponomareva¹,

Klyushnikov²,

Abramycheva³

et al. 2014

Journal of the Neurological Sciences

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Section: Introductionmentioning

confidence: 98%

Alpha–theta border EEG abnormalities in preclinical Huntington's disease

Ponomareva¹,

Klyushnikov²,

Abramycheva³

et al. 2014

Journal of the Neurological Sciences

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“…The empirical validity of LORETA has been established in a large number of studies [86] covering various EEG research fields, such as psychiatry [87,88], neurology [89,90], neuropsychopharmacology [91] and sleep medicine [92]. Further, the co-registration of LORETA with other neuroimaging methods has shown good correspondence of effects [93,94].…”

Section: Pharmaco-eeg – Advanced Topicsmentioning

confidence: 99%

Guidelines for the Recording and Evaluation of Pharmaco-EEG Data in Man: The International Pharmaco-EEG Society (IPEG)

Jobert¹,

Wilson

Ruigt³

et al. 2012

Neuropsychobiology

148

132

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The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories.

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“…The method was presented in the following articles: [9], [10], [11]. The article [1] presents one version of this method.…”

Section: Presentation Of the Problem And Proposed Solutionsmentioning

confidence: 99%

Towards the Applications of Algorithms for Inverse Solutions in EEG Brain-Computer Interfaces

Jagodzińska¹

2013

International Journal of Electronics and Telecommunications

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Abstract-Locating the sources of EEG signals (signal generators), i.e. indicating the places in the brain that the signals come from is the objective of the inverse problem in BCI applications using EEG. The two algorithms based on the methods used in the inverse problem: the linear least squares method and the LORETA 1 method were compared. An analysis of the accuracy of locating the sources generating EEG signals on the basis of the two above mentioned methods was carried out with the use of the MATLAB programme. The findings made it possible to determine both the complexity of calculation involved in the methods under consideration and to compare the accuracy of the results obtained. Tests were done in which the inverse problem was solved on the basis of the data that were entered from the electrodes. Then potentials on electrodes were found by means of solving the forward problem once again Φ(Φ −→ Φ). Moreover, tests were conducted on simulated data describing current density at selected places in the brain. In this case potentials on the electrodes were found by means of solving the forward problem. Subsequently the inverse problem was solved and potentials at selected places in the brain were specified J(J −→ J). In the case of J(J −→ J) only the relative error was examined, while the variance was studied in both cases. As a result of doing the tests, it was proved that relative errors were the same in the SVD and PINV methods, while in the LORETA method the error was similar. The variance computed for these methods was more differentiated for each of the cases, which made it possible to compare the algorithms in a better way. Differentiation of the variances under 0.2 shows that the algorithms that have been analyzed work properly.On the basis of knowing the results of the inverse problem, an attempt was made to make a selection of the best features of the EEG signal which differentiates the classes. In the present work tests were conducted to examine the differentiation of selected classes. Welch's t-statistics was used to differentiate and order them. The results of the tests present the order for three classes of thought tasks, i.e. imagining moving one's left hand, imagining moving one's right hand, imagining generating words beginning with a randomly chosen letter. The present work is an introduction to a wider classification of features which are made with the use of inverse solutions.

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EEG low-resolution brain electromagnetic tomography (LORETA) in Huntington’s disease

Cited by 31 publications

References 80 publications

Alpha–theta border EEG abnormalities in preclinical Huntington's disease

Alpha–theta border EEG abnormalities in preclinical Huntington's disease

Guidelines for the Recording and Evaluation of Pharmaco-EEG Data in Man: The International Pharmaco-EEG Society (IPEG)

Towards the Applications of Algorithms for Inverse Solutions in EEG Brain-Computer Interfaces

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