On the development of EEG based emotion classification

2021

In this paper, emotion recognition using EEG signals has been reviewed. The methods applied, dataset used for simulation, the results obtained along with the limitations and future work/gap is summarised in this review paper. This paves a way for the upcoming researchers to focus on the problems to be solved and the methods to be proposed as a novel new method or could be an integration or hybrid of the existing techniques or algorithms, along with the dataset to be used.

International Research Journal on Advanced Science Hub, 2021

Emotion plays a vital role in medical research and interpersonal communication. Essentially feeling can be communicated verbally like discourse or non-verbally like outward appearance and physiological signals. A human emotion is complex physiological state which involves a physiological response, a person's experience and behavioral change. EEG measures electric current that are generated due to neuronal activities in the human brain. This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals. Our analysis is based on extracted features and classification methods of emotion recognition. We intended that, this study will be useful for newly researchers those entering in the field of emotion recognition.

2020

Emotions result in physical and physiological changes which affect human intelligence and the world around us. Emotions which indicates inner feelings of a person is represented by EEG as a direct brain response to a stimuli. EEG-based emotion recognition is widely used in affect computing to improve communication between machines and human. In this paper we provide a comprehensive overview of methods proposed for emotion recognition using EEG published in last ten years. Our analysis is focused on feature extraction, selection and classification of EEG for emotion recognition. This survey will be a mile stone for researchers in enhancing the development of emotion recognition using EEG.

2009

The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features for classification of all subjects, because of interindividual differences in the signal. We also identified correlation between the classification error and the extroversion-introversion personality trait measured by EPQ-R test. Introverts have lower excitation threshold so we are able to detect the differences in their EEG activity with better accuracy. Furthermore, the use of Kohonen's self-organizing map for visualization is suggested and demonstrated on one subject.

Computational and Mathematical Methods in Medicine

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on...

This project is based on a few brain controlled robot supported Brain computer interfaces (BCI). BCIs are systems which may be want to bypass conventional channels of communication which can also want to supply direct communication and control between the human brain and physical devices by translating different forms of brain activity into commands in real time. With these commands a mobile robot are often controlled. The intention of the project work is to develop a robot which will assist the disabled people in their lifestyle to try to some work independent of others. Here, we analyse the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this patterns are going to be changing which successively produce different electrical waves. A contraction also will generate a singular electrical signal. All these electrical waves are going to be sensed by the brain wave sensor and it will convert the info into packets and transmit through Bluetooth medium. Level analyser unit (LAU) will receive the brain wave data and it'll extract and process the signal using MATLAB platform. Then the control commands are going to be transmitted to the robot module to process. Electroencephalograms (EEG's) used to review brain activity within the context of strokes, epilepsy. It is desirable to eliminate EEG artifacts to enhance signal collection. The emotion recognition system for human brain signals is proposed using EEG signals. We measure EEG signals concerning emotion, divide them into five frequency ranges on the thought of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. With increasing role of brain computer interface applications has growned by the importance and need of automatically recognize emotion from EEG signals. The detection of fine grained changes in functional state of human brain are often detected using EEG signals in comparison to other physiological signals. Under four emotions (disgust, happy, surprise and fear) from the participants the EEG signal is acquired using the audio-visual induction based acquisition protocol. The 63 biosensors are used for registering the EEG signal for various emotions. To classify the human emotions three statistical features are extracted when two different Lifting Based Wavelet Transform (LBWT) is employed after the pre-processing signals. Results confirm the likelihood of using two different lifting scheme based wavelet transform for assessing the human emotions from EEG signals.

Tehnički glasnik

The term "emotion" refers to an individual's response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent.

Indian Journal of Computer Science and Engineering, 2022

Currently researchers have shown immeasurable awareness in Brain Computer Interface (BCI) systems, which enable any user to exchange intelligence and knowledge with surrounding and control instruments by using brain signals; concept is identified as Affective Computing. In this work we are using the SEED database, which is publically available to classify three emotions Positive, Negative and Neutral. Five electrode pairs from various brain regions like Prefrontal, Frontal, Temporal, Parietal and Occipital are selected for this work based on previous research. Diverse time domain and time frequency domain features are extracted from EEG signals. Wavelet Transform (WT) is used to extract a variety of time frequency domain features. Daubechies wavelet function (db6) with 6 levels of decomposition is used to split EEG signals into various frequency bands (δ, θ, α, β and γ). SVM and k-NN algorithms are used as classifiers to estimate classification performance. Hypothetical results illustrate an average classification accuracy of 62.4% for classifying three emotions. Gamma and Beta, the higher frequency bands perform well in emotion recognition.

Machine Learning (ML) approaches have been fruitfully applied to several classification problems of neurophysiological activity. Considering the relevance of emotion in human cognition and behaviour, ML found an important application field in emotion identification based on neurophysiological activity. Nonetheless, the literature results present a high variability depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight on ML applied to emotion identification based on electrophysiological brain activity. For this reason, we recorded EEG activity while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also cont...

journal of King Abdulaziz University Computing and Information Technology Sciences

Human emotions are too complex to be accurately recognized by others. In the era of Artificial Intelligence (AI), automatic emotion recognition has become an active field for research and applications.