Classification of EEG Signals in Seizure Detection System Using Ellipse Area Features and Support Vector Machine

2018 ◽  
pp. 87-96
Author(s):  
Dattaprasad A. Torse ◽  
Veena Desai ◽  
Rajashri Khanai
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Seizure Detection ◽  
Support Vector ◽  
Eeg Signals ◽  
Ellipse Area

scholarly journals SISTEM DETEKSI EMOSI MENGGUNAKAN SINYAL EEG “EMOCLASS”

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ngarap Imanuel Manik ◽  
Antonius Ivan
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Computer Program ◽  
Detection System ◽  
Support Vector ◽  
Emotional Experiences ◽  
Analysis Model ◽  
Eeg Signals ◽  
Emotion Model

An emotional detection system has been developed using EEG signals with the help of a computer program. The results of this development are an important step in progress in learning the classification of emotional detection because it can be obtained more quickly. This study uses a support vector machine approach with a statistical analysis model that can be used to classify emotions into the Russell Emotion Model. Emotions included are Amused, Fear, Calm, Sad, and Neutral. With some assumptions, this system can provide benefits to the multimedia sector by producing applications that automatically detect human emotional experiences.

scholarly journals Wavelet based Extraction of Features from EEG Signals and Classification of Novel Emotion Recognition Using SVM and HMM Classifier and to Measure its Accuracy

2021 ◽  
Vol 36 (1) ◽  
pp. 727-732
Author(s):  
M. Mohanambal ◽  
Dr.P. Vishnu Vardhan
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Mean Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Emotion Detection ◽  
Eeg Signals

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

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