Electroencephalography (EEG) is the tool to record electrical activity over the scalp. This technique is widely used in clinical or research setting, since it is user friendly and non – invasive. In clinical setting, the EEG signal is used to diagnose the disease related to brain. In research setting, the usage of EEG signal is focused on rehabilitation; mental stress study . This paper presented the review on different methods for mental stress level classification. There are four methods for investigation such as principal component analysis, artificial neural network, discrete wavelet transform and spectral centroid technique. The features obtained from methods were extracted from recorded EEG signals and modeled using various classifiers like k-NN and ANN classifier. Based on this four method, we concluded that principal component analysis is better method and it has high accuracy. (98%).

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Eeg Knn Mental Stress Modified Covariance Principal Component Analysis (pca) Neural Network Discrete Wavelet Transform Spectral Centroid Technique.