Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 5 Issue 1
Feature Extraction Techniques and Classification Algorithms for EEG Signals to detect Human Stress - A Review
Chetan Umale Amit Vaidya Shubham Shirude Akshay Raut
10.7753/IJCATR0501.1002
keywords : Stress, DWT, KNN, LDA, Naïve Bayes, EEG signal, NeuroSky Mindwave.
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain. Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms (LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of classifiers, will detect the human stress levels.
@artical{
512016ijcatr05011002,
Title = "Feature Extraction Techniques and Classification Algorithms for EEG Signals to detect Human Stress - A Review",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="1",
Pages ="8 - 14",
Year = "2016",
Authors ="
Chetan Umale
Amit Vaidya
Shubham Shirude
Akshay Raut
"}
The paper proposes a self-diagnosis system for human stress
The paper focuses on the use of low cost device to capture EEG signals.
Combination of classifiers is proposed for more accurate classification.
Different feature extraction and classification system are described and compared.