From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.

International audienceBoth biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations

Unsupervised construction of fuzzy measures through self-organizing feature maps and its application in color image segmentation

AbstractThe paper presents a framework for the segmentation of multi-dimensional images, e.g., color, satellite, multi-sensory images, based on the employment of the fuzzy integral, which undertakes the classification of the input features. The framework makes use of a self-organizing feature map, whereby the coefficients of the fuzzy measure are determined. This process is unsupervised and therefore constitutes one of the main contributions of the paper.The performance of the framework is shown by successfully realizing the segmentation of color images in two different applications. First, the features of the framework and its parameterization are analyzed by segmenting different images used as benchmark in image processing. Finally, the framework is applied in the segmentation of different images taken under difficult illumination conditions. The images serve the development of an automated cashier system, where the weak segmentation constitutes the first step for the identification of different market items. The presented framework succeeds in the segmentation of all these color images

Phytoplankton descrimination from fluorescence spectra using neural networks

Martech 2007 International Workshop on Marine Technology, 15-16 november 2007, Vilanova i la Geltrú, Spain.-- 2 pages, 3 figures, 1 tableThe project VARITEC-SAMPLER (CTM2004-04442-C02-2/MAR) is funded from the Spanish Ministry of Education and SciencePeer reviewe

A rapid technique for classifying phytoplankton fluorescence spectra based on self-organizing maps

11 pages, 13 figures, 7 tablesFluorescence spectroscopy has been demonstrated to be a powerful tool for characterizing phytoplankton communities in marine environments. Using different fluorescence spectra techniques, it is now possible to discriminate the major phytoplankton groups. However, most of the current techniques are based on fluorescence excitation measurements, which require stimulation at different wavelengths and thus considerable time to obtain the complete spectral profile. This requirement may be an important constraint for several mobile oceanographic platforms, such as vertical profilers or autonomous underwater vehicles, which require rapid-acquisition instruments. This paper presents a novel technique for classifying fluorescence spectra based on self-organizing maps (SOMs), one of the most popular artificial neural network (ANN) methods. The method is able to achieve phytoplankton discrimination using only fluorescence emission spectra (single wavelength excitation), thus reducing the acquisition time. The discrimination capabilities of SOM using excitation and emission spectra are compared. The analysis shows that the SOM has a good performance using excitation spectra, whereas data preprocessing is required in order to obtain similar discrimination capabilities using emission spectra. The final results obtained using emission spectra indicate that the discrimination is properly achieved even between algal groups, such as diatoms and dinoflagellates, which cannot be discriminated with previous methods. We finally point out that although techniques based on excitation spectra can achieve a better taxonomic accuracy, there are some applications that require faster acquisition processes. Acquiring emission spectra is almost instantaneous, and techniques such as SOM can achieve good classification performance using appropriately preprocessed dataThis work was supported by the project ANERIS (PIF-015-1), funded by the Spanish National Research Council (CSIC)Peer reviewe

Potential support vector machines and Self-Organizing Maps for phytoplankton discrimination

Neural Networks (IJCNN), The 2010 International Joint Conference on, 18-23 July 2010Fluorescence spectroscopy is a powerful technique usually used to evaluate phytoplankton marine environments. In this study, a kernel method (Potential Support Vector Machine, P-SVM) is presented, evaluating its capability to achieve phytoplankton classification from its fluorescence spectra. Different phytoplankton species were studied, and their fluorescence spectra were acquired in laboratory. In a previous study working with Self-Organizing Maps (SOM), it was proved with experimental data from laboratory that excitation spectra were more discriminative than emission spectra. It was also shown that using some preprocessing techniques, such as derivative analysis, the classification performance from emission fluorescence data can be improved. The classification results were encouraging to keep working with emission fluorescence, and herein we present a comparison between P-SVM and SOM for this goalPeer reviewe

Fast phytoplankton classification from emission fluorescence spectra based on Self-Organizing Maps

OCEANS'08 MTS/IEEE Quebec "Oceans, Poles & Climate: Technological Challenges", 15-18 September 2008, Quebec City, CanadaFluorescence spectroscopy is a powerful technique usually used to evaluate phytoplankton marine environments. In this study, a fast-technique for phytoplankton discrimination is presented based on the Self-Organizing Maps (SOM), evaluating its capability to achieve phytoplankton classification from its emission fluorescence spectra. The aim of this work is to reduce the acquisition time required for some of the existing techniques. Several cultures representing different algae groups were grown under the same conditions and their Emission spectra were measured every day. Finally, SOM analysis combined with derivative analysis was performed obtaining encouraging resultsPeer Reviewe