Study of the Length of time Window in Emotion Recognition based on EEG Signals / Estudio de la Longitud de Ventana de Tiempo en el Reconocimiento de Emociones Basado en Señales EEG

Abstract The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the performance of different classifier models across various TW lengths, based on a dataset of EEG signals extracted from individuals during emotional stimulation. Two types of analyses were conducted: between-subjects and within-subjects. Performance measures such as accuracy, area under the curve, and Cohen's Kappa coefficient were compared among five supervised classifier models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Decision Trees (DT). The results indicate that, in both analyses, all five models exhibit higher performance in TW ranging from 2 to 15 seconds, with the 10 seconds TW particularly standing out for between-subjects analysis and the 5-second TW for within-subjects; furthermore, TW exceeding 20 seconds are not recommended. These findings provide valuable guidance for selecting TW in EEG signal analysis when studying emotions.

Resumen El objetivo de esta investigación es presentar un análisis comparativo empleando diversas longitudes de ventanas de tiempo (VT) durante el reconocimiento de emociones, utilizando técnicas de aprendizaje automático y el dispositivo de sensado inalámbrico portátil EPOC+. En este estudio, se utilizará la entropía como característica para evaluar el rendimiento de diferentes modelos clasificadores en diferentes longitudes de VT, basándose en un conjunto de datos de señales EEG extraídas de individuos durante la estimulación de emociones. Se llevaron a cabo dos tipos de análisis: entre sujetos e intra-sujetos. Se compararon las medidas de rendimiento, tales como la exactitud, el área bajo la curva y el coeficiente de Cohen's Kappa, de cinco modelos clasificadores supervisados: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) y Decision Trees (DT). Los resultados indican que, en ambos análisis, los cinco modelos presentan un mayor rendimiento en VT de 2 a 15 segundos, destacándose especialmente la VT de 10 segundos para el análisis entre los sujetos y 5 segundos intrasujetos; además, no se recomienda utilizar VT superiores a 20 segundos. Estos hallazgos ofrecen una orientación valiosa para la elección de las VT en el análisis de señales EEG al estudiar las emociones.

Use of Biomedical Engineering for Rehabilitation of Patients with Disability Caused by Guillain-Barré Syndrome: a Systematic Review / Uso de la Ingeniería Biomédica para Rehabilitación de Pacientes con Discapacidad Causada por el Síndrome de Guillain-Barré: Una Revisión Sistemática

Abstract This systematic review aims to assess the extent to which biomedical engineering has been applied in the rehabilitation of patients suffering from Guillain-Barré Syndrome (GBS), given the scarcity of information on this topic. We conducted a thorough analysis of research articles, conference abstracts, and case reports published between 2000 and 2023, specifically from ScienceDirect, PubMed, IEEE Xplore, Springer, and Dimensions. 19 articles were extensively discussed, complemented by an additional 40 information sources providing supplementary information. Each paper underwent a meticulous review process by the four authors, where each separately examined the title and abstract of the papers and subsequently provided a thorough examination of the full text; when conflicts arose, a clear consensus was reached through discussion. The analysis of the articles revealed a notable improvement in upper and lower limb function of GBS patients that was facilitated by both custom-made and commercial devices. Likewise, a small handful of other devices have been used (e.g., to improve urinary retention issues). There is a clear opportunity for new research, innovation and applications.

Resumen Esta revisión sistemática tiene como objetivo evaluar hasta qué punto se ha aplicado la ingeniería biomédica en la rehabilitación de pacientes que padecen el Síndrome de Guillain-Barré (SGB), dada la escasez de información sobre este tema. Realizamos un análisis exhaustivo de artículos de investigación, resúmenes de conferencias e informes de casos publicados entre 2000 y 2023, específicamente de ScienceDirect, PubMed, IEEE Xplore, Springer y Dimensions. Se discutieron ampliamente 19 artículos, complementados con 40 fuentes de información adicionales. Cada artículo pasó por un meticuloso proceso de revisión por parte de los cuatro autores, donde cada uno examinó por separado el título y el resumen de los artículos y posteriormente proporcionó un examen exhaustivo del texto completo; cuando surgieron conflictos, se alcanzó un consenso mediante la discusión. El análisis de los artículos reveló una mejora notable en la función de las extremidades superiores e inferiores de los pacientes con SGB que fue facilitada por dispositivos tanto hechos a medida como comerciales. Asimismo, se han creado un pequeño puñado de otros dispositivos, (por ejemplo, para mejorar los problemas de retención urinaria). Existe una clara oportunidad para nueva investigación, innovación y aplicaciones.

A Deep Learning Approach for Predicting Multiple Sclerosis.

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

HRpI System Based on Wavenet Controller with Human Cooperative-in-the-Loop for Neurorehabilitation Purposes.

There exist several methods aimed at human-robot physical interaction (HRpI) to provide physical therapy in patients. The use of haptics has become an option to display forces along a given path so as to it guides the physiotherapist protocol. Critical in this regard is the motion control for haptic guidance to convey the specifications of the clinical protocol. Given the inherent patient variability, a conclusive demand of these HRpI methods is the need to modify online its response with neither rejecting nor neglecting interaction forces but to process them as patient interaction. In this paper, considering the nonlinear dynamics of the robot interacting bilaterally with a patient, we propose a novel adaptive control to guarantee stable haptic guidance by processing the causality of patient interaction forces, despite unknown robot dynamics and uncertainties. The controller implements radial basis neural network with daughter RASP1 wavelets activation function to identify the coupled interaction dynamics. For an efficient online implementation, an output infinite impulse response filter prunes negligible signals and nodes to deal with overparametrization. This contributes to adapt online the feedback gains of a globally stable discrete PID regulator to yield stiffness control, so the user is guided within a perceptual force field. Effectiveness of the proposed method is verified in real-time bimanual human-in-the-loop experiments.