Classification and Determination of Human Emotional States using EEG (original) (raw)

Emotion Analysis using Different Stimuli with EEG Signals in Emotional Space

Automatic detection for human-machine interfaces of the emotional states of the people is one of the difficult tasks. EEG signals that are very difficult to control by the person are also used in emotion recognition tasks. In this study, emotion analysis and classification study were conducted by using EEG signals for different types of stimuli. The combination of the audio and video information has been shown to be more effective about the classification of positive/negative (high/low) emotion by using wavelet transform from EEG signals, and true positive rate of 81.6% was obtained in valence dimension. Information of audio was found to be more effective than the information of video at classification that is made in arousal dimension, and true positive rate of 73.7% was obtained when both stimuli of audio and audio+video are used. Four class classification performance has also been examined in the space of valence-arousal.

Human Emotion Detection via Brain Waves Study by Using Electroencephalogram (EEG)

International Journal on Advanced Science, Engineering and Information Technology, 2016

Human emotion is very difficult to determine just by looking at the face and also the behavior of a person. This research was conducted to detect or identify human emotion via the study of brain waves. In addition, the research aims to develop computer software that can detect human emotions quickly and easily. This study aims at EEG signals of relationship and human emotions. The main objective of this recognition is to develop "mind-implementation of Robots". While the research methodology is divided into four; (i) both visibility and EEG data were used to extract the date at the same time from the respondent, (ii) the process of complete data record includes the capture of images using the camera and EEG, (iii) pre-processing, classification and feature extraction is done at the same time, (iv) the features extracted is classified using artificial intelligence techniques to emotional faces. Researchers expect the following results; (i) studies brain waves for the purpose of emotions, (ii) the study of human emotion with facial emotions and to relate the brain waves, (iii). In conclusion, this study is very useful for doctors in hospitals and police departments for criminal investigation. As a result of this study, it also helps to develop a software package.

Classification of Human Emotions using EEG Signals

IJCA, 2016

In this paper we proposed new features based on wavelet transform for classification of human emotions (disgust, happy, surprise, fear and neutral). from electroencephalogram (EEG) signals.EEG signals are collected using 64 electrodes from twenty subjects and are placed over the entire scalp using International 10-10 system or international 10-20 system. The EEG signals are preprocessed using filtering methods to remove the noise. Feature extraction of the principle signal is done by using methods such as wavelet transform. The feature extracted signals are then classified using Neural Network (NN) and the neural system is trained and we get trained classifier according to the classification of the signals and the results are obtained. To test the signal the feature extracted signals are given directly to the trained classifier and results are obtained.

EMOTION RECOGNITION USING EEG SIGNALS

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.

Emotion recognition using electroencephalogram signal

Indonesian Journal of Electrical Engineering and Computer Science, 2019

Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then,...

An Empirical Study on EEG Signals for Emotion

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.

Analysis of emotion disorders based on EEG signals of Human Brain

International Journal of Computer Science, Engineering and Applications, 2012

In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.

Individual Classification of Emotions Using EEG

Many studies suggest that EEG signals provide enough information for the detection of human emotions with feature based classification methods. However, very few studies have reported a classification method that reliably works for individual participants (classification accuracy well over 90%). Further, a necessary condition for real life applications is a method that allows, irrespective of the immense individual difference among participants, to have minimal variance over the individual classification accuracy. We conducted offline computer aided emotion classification experiments using strict experimental controls. We analyzed EEG data collected from nine participants using validated film clips to induce four different emotional states (amused, disgusted, sad and neutral). The classification rate was evaluated using both unsupervised and supervised learning algorithms (in total seven “state of the art” algorithms were tested). The largest classification accuracy was computed by means of Support Vector Machine. Accuracy rate was on average 97.2%. The experimental protocol effectiveness was further supported by very small variance among individual participants’ classification accuracy (within interval: 96.7%, 98.3%). Classification accuracy evaluated on reduced number of electrodes suggested, consistently with psychological constructionist approaches, that we were able to classify emotions considering cortical activity from areas involved in emotion representation. The experimental protocol therefore appeared to be a key factor to improve the classification outcome by means of data quality improvements.

Emotion recognition based on EEG signals during watching video clips

Brain signal analysis for human emotion recognition plays important role in psychology , management and human machine interface design. Electroencephalogram (EEG) is the reflection of brain activity – by studying and analysing these signals we are able to perceive emotional state changes. In order to do so, it is necessary to select the appropriate EEG channels that are placed mostly on the frontal part of the head. In this paper we used video stimuli to induce happy and sad mood of 20 participants. To classify the emotions experienced by the volunteers we used five different classification methods to obtain optimal result taking into account all features that were extracted from signals. We observed that the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) obtained the highest accuracy of emotion recognition.

Classification of the emotional states based on the EEG signal processing

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.