In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's “intentions” by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.
In this article, we present a comprehensive measurement system to determine the level of user emotional arousal by the analysis of electrodermal activity (EDA). A number of EDA measurements were collected, while emotions were elicited using specially selected movie sequences. Data collected from 16 participants of the experiment, in conjunction with those from personal questionnaires, were used to determine a large number of 20 features of the EDA, to assess the emotional state of a user. Feature selection was performed using signal processing and analysis methods, while considering user declarations. The suitability of the designed system for detecting the level of emotional arousal was fully confirmed, throughout the number of experiments. The average classification accuracy for two classes of the least and the most stimulating movies varies within the range of 61‒72%.