Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Abstract

Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold crossvalidation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio-video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.

Classifying BCI signals from novice users with extreme learning machine

“…The dataset is composed by EEG signals from 5 right-handed males volunteers (21,30,30,33,33 years old) with normal vision implementing a Motor Imagery task. They have imagined the right and left hand movement according to a predefined timing.…”

“…Thus the analysis of the EEG signal has been used to detect abnormal functions of the brain like disorders such as autism, dementia, schizophrenic, depression etc... [8,[19][20][21]. Besides medical applications, the information extracted in this type of analysis has been used for nonmedical purposes such as mental fatigue, emotion recognition or BCI.…”

Density Driven Image Coding for Tumor Detection in mri Image

“…clusters, and shape features were extracted for the DWT bands. For each set of features, most discriminating features and their optimal weights were found using real-valued and binary genetic algorithms (GA) utilizing a k-nearest-neighbor classifier.In [6] a new method for classification of human emotions based on multiwavelet transform of magnetic resonance imaging (MRI) images is proposed. The extracted features used as an input to multiclass least squares support vector machine (MC-LS-SVM) for classification of human emotions.…”

Induction of emotional states in people with disabilities through film clips using brain computer interfaces

“…Algunos de estos métodos procesan imágenes para interpretar las expresiones faciales [16], [17], [22], [23] y otros para interpretar la voz [18]. Sin embargo, estos métodos no son aplicables en personas con parálisis cerebral debido a que, por su condición, afecta su capacidad de controlar expresiones faciales, sus cuerdas vocales, así como las redes de nervios y músculos que se extienden por todo su cuerpo [14]. Otro de los enfoques para el reconocimiento de emociones, es el análisis de señales fisiológicas.…”

“…Otro de los enfoques para el reconocimiento de emociones, es el análisis de señales fisiológicas. En [14] se argumenta que ese tipo de análisis es un medio más natural de reconocimiento de emociones, puesto que, el estado emocional se refleja inherentemente en la actividad del sistema nervioso.…”