Analysis of possibility of virtual reality usage for investigating reaction on given conditions

The study was tend to checking the possibility of using VR technology to invoke specific emotions on the subject. Literature studies have shown a high probability of obtaining successful results, but so far studies have been based on measurements obtained using specialized, expensive equipment. This study was about to show that obtaining similar results is also possible with usage of low budget solutions. Received results allow to conclude even using the less sophisticated technology, satisfactory results can be gained, which can determine the effect of the image displayed in the goggles on the emotions of the subject

A real-time wearable emotion detection headband based on EEG measurement

A real-time emotion detection system based on electroencephalogram (EEG) measurement has been realised by means of an emotion detection headband coupled with printed signal acquisition electrodes and open source signal processing software (OpenViBE). Positive and negative emotions are the states classified and the Theta, Alpha, Beta and Gamma frequency bands are selected for the signal processing. It is found that, by using a combination of Power Spectral Density (PSD), Signal Power (SP) and Common Spatial Pattern (CSP) as the features, the highest subject-dependent accuracy (86.83%) and independent accuracy (64.73%) is achieved, when using Linear Discrimination Analysis (LDA) as the classification algorithm. The standard deviation of the results is 5.03. The electrode locations were then improved for the detection of emotion, by moving them from F1, F2, T3 and T4 to A1, F2, F7 and F8. The subject-dependent accuracy, using the improved locations, increased to 91.75% from 86.83% and 75% of participants achieved a classification accuracy higher than 90%, compared with only 16% of participants before improving the electrode arrangement

EMOTION RECOGNITION BASED ON VARIOUS PHYSIOLOGICAL SIGNALS - A REVIEW

Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition

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