Three-dimensional interpolation methods to spatiotemporal EEG mapping during various behavioral states

Nouira, Ibtihel; Abdallah, Asma Ben; Bedoui, Mohamed Hédi

doi:10.1007/s11760-015-0844-7

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…For animal brain mapping we used two types of spline interpolation: spherical splines and 3D splines. Both of these methods are used in human brain mapping but not applied in rats yet [16][17][18][19][20].…”

Section: Animal Brain Mappingmentioning

confidence: 99%

“…However the rats brain can not be approximated well by a simple sphere. Based on results in the studies [18], [19] we can say, that we get better results when we use 3D splines for brain mapping in humans. In this paper, the 3D splines are applied in rats for the first time.…”

Section: Introductionmentioning

confidence: 99%

“…suggested methods of spherical spline and 3D spline interpolation [16], [17]. Spherical splines are the most used interpolation methods for brain mapping in humans [18], [19]. However the rats brain can not be approximated well by a simple sphere.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the 3D splines are applied in rats for the first time. Efficiency of interpolation methods is computed by using Root Mean Squared error (RMS) between measured and interpolated signals under the electrodes [18], [19]. First step in brain mapping with spherical splines and 3D splines is the projection on the sphere [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Methods for Animal Brain Mapping

Tylš¹,

Krajča²,

Páleníček³

et al. 2018

RADIOENGINEERING

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Measurements of brain electrical activity in animals are essential for the validation of the pharmaco-effect of drugs. The way to evaluate these recordings should be comparable to that of EEG in humans. Methods that visualize the results of the measured EEG recording include brain mapping in two-dimensional or three-dimensional space. The most commonly used methods of interpolation techniques in humans are spherical splines and 3D splines. We measured nine brains of Wistar rats and compared them with a brain model from the atlas (Brain Atlas Reconstructor, BAR). We validated the brain model of Wistar rat for future use. We implemented a module in MATLAB 2015a for brain mapping, specifically, we implemented algorithms for spherical and 3D spline mapping. The root mean square error of the spherical spline method is 0.5943 in the case of testing signal and 0.6291/0.6388 in the case of real data estimation. The root mean square error of the 3D spline method is 0.4334 in the case of testing signal and 0.0849/0.0768 in the case of real data estimation. Our results show that the 3D spline method with the projection on sphere gives significantly better 3D potential map than spherical splines.

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Section: Animal Brain Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methods for Animal Brain Mapping

Tylš¹,

Krajča²,

Páleníček³

et al. 2018

RADIOENGINEERING

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Hybrid Multi-Channel EEG Filtering Method for Ocular and Muscular Artifact Removal Based on the 3D Spline Interpolation Technique

Abidi

Nouira

Assali

et al. 2021

The Computer Journal

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The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.

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