Analysis of emotion disorders based on EEG signals of Human Brain (original) (raw)
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Classification and Determination of Human Emotional States using EEG
International Journal of Medical Science, 2017
Emotion plays an important role in everybody's life and in this paper I show how the brain waves tells us which emotion is being experienced by a person. This research studies the brain waves in happy, sad and disgust emotion and determines how the brain waves pertaining to the emotions are distinguishable from each other and how the brain waves of a single emotion is consistent (or inconsistent) from person to person. In doing this research EEG machine is used and video clips are used to instigate the different emotions.
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.
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.
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,...
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.
Review on Emotion Recognition using EEG Signals
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.
Biomedical Engineering and …, 2010
this paper proposes a new emotional stress recognition system using multi-modal bio-signals. Since electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research, it is used as the main signal. In order to choose the proper EEG channels we used the cognitive model of the brain under emotional stress. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological signals under pictures induction environment (calm-neutral and negative-excited) for participants. Qualitative and quantitative evaluation of psychophysiological signals have been tried to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After preprocessing the signals, both Linear and nonlinear features were employed to extract the EEG parameters. Wavelet coefficients and chaotic invariants like fractal dimension by Higuchi's algorithm and correlation dimension were used to extract the characteristics of the EEG signal which showed that the classification accuracy in two emotional states was 82.7% using the Elman classifier. This is a great improvement in results compared with other similar published work.
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.
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.
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.