Detection and Classification of Human Stress Analysis using EEG Signals

Authors

  • A. Rafega Beham  Sr.Assistant Professor, Dept.of Information Science & Engg, New Horizon College of Engineering, Bangalore

Keywords:

Stress analysis, EEG signals, ocular effect, feature extraction, classification, SVM

Abstract

In day to day lifestress plays significant role in the quality of human life. Emotion plays a major role in motivation, perception, cognition, creativity,attention, learning and decision-making.In recent years, stress analysis by using electro-encephalography (EEG) signalshas emerged as an importantareaof research. EEG signalsare one of the most important means of indirectly measuring the state of the brain. EEG (Electroencephalogram) signal is a neuro-signal which is produced due the different electrical exercises in the mind. These signals canbe captured and processed to get the necessary data which can be used to detect some psychological changes in early stage. In this proposed system, EEG signal dataset is pre-processed and components with ocular effect are extracted from the EEG signal. Classification of stress level is accomplished by applying SVM (Support-Vector Machine) algorithm which gives the better accuracy.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-12-30

Issue

Volume 4, Issue 9, November-December-2019

Section

Research Articles

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How to Cite

[1]
A. Rafega Beham, " Detection and Classification of Human Stress Analysis using EEG Signals" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 9, pp.687-691, November-December-2019.