Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

2020 ◽  
Vol 42 (11) ◽  
pp. 2057-2067
Author(s):  
Moon Inder Singh ◽  
Mandeep Singh
Keyword(s):  
Event Related Potentials ◽  
Central Line ◽  
Support Vector ◽  
Eeg Signals ◽  
High Arousal ◽  
Brain Signals ◽  
Polynomial Classifier ◽  
Related Potentials ◽  
Daunting Challenge

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

scholarly journals SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL

2017 ◽  
Vol 10 (13) ◽  
pp. 137
Author(s):  
Darshan A Khade ◽  
Ilakiyaselvan N
Keyword(s):  
Artificial Intelligence ◽  
Event Related Potentials ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signals ◽  
Future Technology ◽  
Brain Waves ◽  
Brain Signals ◽  
Related Potentials ◽  
Signal Machine

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. 

Classification of Brain Signals of Event Related Potentials using Different Methods of Feature Extraction

2017 ◽  
Vol 8 (4) ◽  
pp. 680-686
Author(s):  
Ishfaque Ahmed ◽  
Muhammad Jahangir ◽  
Syed Tanveer Iqbal ◽  
Muhammad Azhar ◽  
Imran Siddiqui
Keyword(s):  
Feature Extraction ◽  
Event Related Potentials ◽  
Brain Signals ◽  
Related Potentials

scholarly journals Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures

2021 ◽  
Vol 11 (9) ◽  
pp. 4306
Author(s):  
Irina E. Nicolae ◽  
Mihai Ivanovici
Keyword(s):  
Subjective Evaluation ◽  
Event Related Potentials ◽  
Texture Image ◽  
Eeg Signals ◽  
Wireless System ◽  
Image Complexity ◽  
Practical Applications ◽  
Non Invasive ◽  
Brain Signals ◽  
Related Potentials

In practical applications, such as patient brain signals monitoring, a non-invasive recording system with fewer channels for an easy setup and a wireless connection for remotely monitor physiological signals will be beneficial. In this paper, we investigate the feasibility of using such a system in a visual perception scenario. We investigate the complexity perception of color natural and synthetic fractal texture images, by studying the correlations between four types of data: image complexity that is expressed by computed color entropy and color fractal dimension, human subjective evaluation by scoring, and the measured brain EEG responses via Event-Related Potentials. We report on the considerable correlation experimentally observed between the recorded EEG signals and image complexity while considering three complexity levels, as well on the use of an EEG wireless system with few channels for practical applications, with the corresponding electrodes placement in accordance with the type of neural activity recorded.

BAYESIAN CLASSIFICATION OF SINGLE-TRIAL EVENT-RELATED POTENTIALS IN EEG

2004 ◽  
Vol 14 (02) ◽  
pp. 719-726 ◽  
Author(s):  
JENS KOHLMORGEN ◽  
BENJAMIN BLANKERTZ
Keyword(s):  
Event Related Potentials ◽  
Computer Interface ◽  
Support Vector ◽  
Single Trial ◽  
Data Set ◽  
Classifier Design ◽  
Event Class ◽  
Related Potentials ◽  
Straightforward Approach

We present a systematic and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. Instead of using a generic classifier off-the-shelf, like a neural network or support vector machine, our classifier design is guided by prior knowledge about the problem and statistical properties found in the data. In particular, we exploit the well-known fact that event-related drifts in EEG potentials, albeit hard to detect in a single trial, can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event class and use Bayes' decision rule for the classification of new and unlabeled data. The method is successfully applied to a data set from the NIPS*2001 Brain–Computer Interface post-workshop competition. Our result turned out to be competitive with the best result of the competition.

scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

2021 ◽  
Vol 15 ◽  
Author(s):  
Zongya Zhao ◽  
Jun Li ◽  
Yanxiang Niu ◽  
Chang Wang ◽  
Junqiang Zhao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Event Related Potentials ◽  
Single Type ◽  
Support Vector ◽  
Phase Lag ◽  
Related Potentials

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

scholarly journals Classification of Emotions through EEG Signals using SVM and DNN

2020 ◽  
Vol 8 (5) ◽  
pp. 206-209
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Emotional State ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Work Place ◽  
Support Vector ◽  
Eeg Signals ◽  
Brain Signals

Emotions are important for Humans both at work place and in their life. Emotions helps us to communicate with others, to take decisions, in understand others etc., Emotions recognition not only helps us to solve the mental illness but also are important in various application such as Brain Computer Interface , medical care and entertainment This paper mainly deals with how Emotions are Classified through EEG Signals using SVM (Support Vector machine) and DNN (Deep Neural Networks) . Applying the most appropriate algorithm to detect the emotional state of a person and play the corresponding song in the playlist. Brain signals can be collected using EEG (electroencephalography) devices

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