Study of Emotional Variability Using Photoplethysmogram Signal

This study investigates the feasibility of photoplethysmogram (PPG) signals in recognizing variability in human’s funny, fear and sad emotions. Undoubtedly, Easy Pulse data acquisition device which is used to perceive the PPG signals have superior criterions which are small in size, low power consumption as well as low in cost. Thus, this study will prove the robustness and reliability of PPG signals as an emotion recognition mechanism. A total of ten subjects were chosen randomly which ranged from twenty-one to twenty-four years old. A total of five male and five female students were given three different videos to stimulate different emotions during the given time. Easy Pulse sensor, which has the ability in filtering the unwanted signals has made the study easier. Discriminative features are then extracted from the PPG morphology. PPI, maximum amplitude, as well as the Cardioid pattern of the signals. Finally, four methods of classification have been used to identify the variability in emotions. PPI, maximum amplitude, area and maximum radius of the Cardioid loop were used as the classifiers. These methods have clearly shown great results in differentiating between funny, fear and sad emotions. It was discovered that every human has different rate of sensitivity to fear and sad. Some have the tendency to be very sensitive to fear and some to sad. The experimental results demonstrated that the physiological signals such as PPG have great potentials where the system provides high classification performance

An efficient emotion classification system using EEG

Emotion classification via Electroencephalography (EEG) is used to find the relationships between EEG signals and human emotions. There are many available channels, which consist of electrodes capturing brainwave activity. Some applications may require a reduced number of channels and frequency bands to shorten the computation time, facilitate human comprehensibility, and develop a practical wearable. In prior research, different sets of channels and frequency bands have been used. In this study, a systematic way of selecting the set of channels and frequency bands has been investigated, and results shown that by using the reduced number of channels and frequency bands, it can achieve similar accuracies. The study also proposed a method used to select the appropriate features using the Relief F method. The experimental results of this study showed that the method could reduce and select appropriate features confidently and efficiently. Moreover, the Fuzzy Support Vector Machine (FSVM) is used to improve emotion classification accuracy, as it was found from this research that it performed better than the Support Vector Machine (SVM) in handling the outliers, which are typically presented in the EEG signals. Furthermore, the FSVM is treated as a black-box model, but some applications may need to provide comprehensible human rules. Therefore, the rules are extracted using the Classification and Regression Trees (CART) approach to provide human comprehensibility to the system. The FSVM and rule extraction experiments showed that The FSVM performed better than the SVM in classifying the emotion of interest used in the experiments, and rule extraction from the FSVM utilizing the CART (FSVM-CART) had a good trade-off between classification accuracy and human comprehensibility

Extracción de características de señales EEG para el reconocimiento de emociones

En el estudio realizado analizamos la importancia de caracterizar las emociones del ser humano de forma automática y haciendo uso de registros EEG (Electroencefalografía) con el fin de realizar una exploración neurofisiológica basada en el registro de la actividad bioeléctrica cerebral. Lo hacemos con el objetivo de ayudar a diagnosticar y tratar enfermedades que impiden que se desarrolle la capacidad de expresar las emociones al mundo exterior y para buscar estados emocionales neutros que nos permitan ser más conscientes de los problemas y nos facilitan poder encontrar soluciones. Muchos estudios han analizado imágenes físicas de los individuos cuando experimentan algún tipo de emoción, pero sacar conclusiones válidas de esto es sumo complicado, ya que incluso cualquiera de nosotros, sin tener ningún tipo de enfermedad asociada, somos capaces de mostrar felicidad cuando la tristeza nos invade. Por este motivo, el estudio de señales fisiológicas se ha vuelto esencial para intentar combatir el problema anteriormente mencionado. Sin embargo, como queda demostrado en el desarrollo de este trabajo, la mayoría de las señales fisiológicas; como puede ser la presión arterial, el estudio de la respiración o la temperatura corporal, cambian con factores ambientales y no solamente emocionales. Es por eso por lo que el estudio de señales encefalográficas (EEG) ha sufrido un auge en los últimos tiempos debido a la enorme relación que tienen los procesos que se dan en el cerebro, sobretodo en el sistema límbico, con la generación de las emociones. Durante el desarrollo de este estudio hemos replicado un algoritmo ya existente. Hemos construido una máquina de decisión que en base a unas características basadas en el procesamiento de señales EEG clasifica automáticamente el estado emocional en cuatro clases. La máquina se ha construido usando una base de datos etiquetada denominada ‘DEAP’. Aunque como veremos posteriormente, todavía queda margen de mejora y será necesario estudiar más características para encontrar una solución al problema sin que exista cabida a la duda.In the study carried out, we analyzed the importance of characterizing human emotions automatically and using EEG (Electroencephalography) records in order to carry out a neurophysiological examination based on the recording of brain bioelectric activity. We do this with the aim of helping to diagnose and treat diseases that prevent the ability to express emotions to the outside world from developing and to seek neutral emotional states that allow us to be more aware of problems and facilitate us to find solutions. Many studies have analyzed physical images of individuals when they experience some kind of emotion, but drawing valid conclusions from this is extremely complicated, since even any of us, without having any type of associated disease, are able to show happiness when sadness invades. For this reason, the study of physiological signals has become essential to try to combat the aforementioned problem. However, as demonstrated in the development of this work, most of the physiological signals; such as blood pressure, the study of breathing or body temperature, change with environmental factors and not only emotional ones. That is why the study of encephalographic signals (EEG) has boomed in recent times due to the enormous relationship that the processes that occur in the brain, especially in the limbic system, have with the generation of emotions. . During the development of this study we have replicated an existing algorithm. We have built a decision machine that based on features based on EEG signal processing automatically classifies the emotional state into four classes. The machine has been built using a labeled database called 'DEAP'. Although, as we will see later, there is still room for improvement and it will be necessary to study more characteristics to find a solution to the problem without room for doubt.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació