Although musical neurofeedback is used in multiple works, few systems have been developed for stress regulation, and no systems have been developed for memory stimulation. For this reason, a music-based neurofeedback system for stress regulation and memory stimulation is proposed. This system was designed as a response to a previous research called “Neurophysiology of Emotions and Intimate Partner Violence (IPV) against Women”. The designed system uses 8 EEG channels to analyze alpha and theta brain-waves from 4 areas of the brain: prefrontal, frontal, temporal and central. By recording a 30 seconds baseline, the system is capable to detect changes in the EEG signal that can be used for the interaction. For feedback, three musical features are modified depending on the EEG analysis: tempo, loudness and loudness of the voice of the singer. For testing the system, two protocols were designed, these protocols focused on memory stimulation and stress regulation. They were designed specifically for each one of the three types of feedback. These protocols were applied on two women (43 and 52 years old), both had been part of the previous project. Results are promising, showing changes in the EEG signals of the participants when comparing the first session and the last one. Changes in performance of some specific tasks in the protocols, show an adequate usability of the system. Further studies will be carry on in order to evaluate long-term effects of the system with more activities.
The use of complex classification algorithms such as deep learning techniques does not allow the researchers to identify the most discriminant features for tumor classification as they lack interpretability. This study aims to develop an algorithm capable of differentiating a set of dermoscopic images depending on whether the tumor is benign or malignant. The priority of this research is to obtain the importance of each extracted feature. This work is focused on the ABCD rule feature analysis and it aims to find the relevance of each feature and its performance in a classification model. A relevant aspect of this study is the use of a heterogeneous database, where the images were uploaded by different sources worldwide. A combination of novel and previously used features are analyzed and their importance is computed by the use of a Gaussian mixture model. After selecting the most discriminant features, a set of classification models was applied to find the best model with the less quantity of features. We found that a total of 65.89% of the features could be omitted with a loss in accuracy, sensibility and specificity equal or lower than 2%. While similar performance measures have been employed in other studies, most results are not comparable, as the databases used were more homogeneous. In the remaining studies, sensitivity values are comparable, with the main difference that the proposed model is interpretable.
Juan López López, D. Carolina Cárdenas-Poveda, María Paula Acero Triviño, Alexandra González-Álvarez, Alejandra Rizo-Arévalo, Mayerli Andrea Prado-Rivera, Eliana Mejía-Soto, Jose Luis Velazquez-Perez, Catalina Espitia
Intimate Partner Violence (IPV) against women is a major problem in Colombia. Nowadays the question about the effects of violence on women and the identification of latent risks that affect their health, is increasingly relevant. This article describes a pilot study that aimed to measure electrophysiological signals corresponding to the emotional neurophysiological response of women who had experienced IPV in contrast to those who did not. Six healthy female adults, ranged in age from 18 to 55 years old enrolled in this study. For the measurement we used the International Affective Picture System (IAPS) and an Auditory and Visual Emotional Memory Test (avEMT), and we recorded the EEG signal with a g.Nautilus 32 g.LadyBird. EEG signals acquired from baseline and during the tests were compared. As a result of IAPS test, we found for all the participants a higher power spectrum at low EEG frequencies and a decrease in power as the frequencies increase for baseline and emotional pictures. For the avMET, both groups show a higher power spectrum in the different phases of the task compared with the baseline, with an exception of one participant from the WIPV group who show the opposite tendency. Also, two machine learning models were trained and an accuracy of more than 85% were achieved to classify EEG signals from women who experienced IPV and women who had not. This research is an approach to the phenomenon of violence against women and broadens the understanding of the effects on emotional response and electrophysiological activation in women who have experienced this type of situation.
The comet assay is a commonly used technique in molecular and cell biology fields, for studies in which the DNA damage of a cell is measured. For instance, it is useful to analyze whenever a carcinogenic cell is affected by chemical agents, helping with oncology research. Traditionally, in order to evaluate the damage of a cell, an expert observes the morphology and the intensity (brightness) of the resulting comet. However, taking into account that a large number of images have to be analyzed, this task may demand a lot of time to be done manually. In recent years, the comet assay analysis has been implemented semi-automatically and automatically with the rise of new image processing algorithms. Although these new algorithms reduce the time invested in the image analysis, some problems in comet identification and accurate measure of their components need to be improved. This project aimed to develop an algorithm and an interface, named CometLab, for flexible automatic comet segmentation. Its performance was assessed with a set of images and compared against an open source, available software called OpenComet. It was found that only 1 of the 15 features that were extracted by both algorithms was not statistically correlated (head diameter), meaning that the designed application is suitable; therefore, this research helped to obtain information about the performance of CometLab in comparison to OpenComet, which serves as setpoint for future works in which it would be possible to decide which algorithm is better.
Movement intention (MI) is the mental state in which it is desired to make an action that implies movement. There are certain signals that are directly related with MI; mainly obtained in the primary motor cortex. These signals can be used in a brain-computer interface (BCI). BCIs have a wide variety of applications for the general population, classified in two groups: optimization of conventional neuromuscular performances and enhancement of conventional neuromuscular performances beyond normal capacities. The main goal of this project is to analyze if neural rhythm modulation enhancement could be achieved by practicing, through a BCI based on MI detection, which was designed in a previous study. A six-session experiment was made with eight healthy subjects. Each session was composed by two stages: a training stage and a testing stage, which allowed control of a videogame. The scores in the game were recorded and analyzed. Changes in alpha and beta bands were also analyzed in order to observe if attention could in fact be enhanced. The obtained results were partially satisfactory, as most subjects showed a clear improvement in performance at some point in the trials. As well, the alpha to beta wave ratio of all the tasks was analyzed to observe if there are changes as the experiment progresses. The results are promising, and a different protocol must be implemented to assess the impact of the BCI on the attention span, which can be analyzed with the alpha and beta waves.
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