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 Classification of event-related potentials using multivariate autoregressive modeling combined with simulated annealing

2003 ◽  
Vol 13 (1) ◽  
pp. 7-11 ◽  
Author(s):  
C.E. Vasios ◽  
O.K. Matsopoulos ◽  
K.S. Nikita ◽  
N. Uzunoglu
Keyword(s):  
Feature Extraction ◽  
Simulated Annealing ◽  
Drug Users ◽  
Back Propagation ◽  
Event Related Potentials ◽  
Back Propagation Algorithm ◽  
Related Potentials ◽  
And Control ◽  
Multivariate Autoregressive

In the present work, a new method for the classification of Event Related Potentials (ERPs) is proposed. The proposed method consists of two modules: the feature extraction module and the classification module. The feature extraction module comprises the implementation of the Multivariate Autoregressive model in conjunction with the Simulated Annealing technique, for the selection of optimum features from ERPs. The classification module is implemented with a single three-layer neural network, trained with the back-propagation algorithm and classifies the data into two classes: patients and control subjects. The method, in the form of a Decision Support System (DSS), has been thoroughly tested to a number of patient data (OCD, FES, depressives and drug users), resulting successful classification up to 100%.

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.

Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU
Keyword(s):  
Event Related Potentials ◽  
Related Potentials ◽  
Spatio Temporal ◽  
Temporal Feature

A new approach for the classification of event related potentials for valid and paradox reasoning

2016 ◽  
Vol 36 (1) ◽  
pp. 292-301
Author(s):  
C. Papaodysseus ◽  
S. Zannos ◽  
F. Giannopoulos ◽  
D. Arabadjis ◽  
P. Rousopoulos ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
New Approach ◽  
Related Potentials

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. 

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