Lab View based Approach for EEG Signal Frequency Identification and Feature Extraction to Detect Mental Stress Level

The continuous monitoring of an EEG signal and estimation of vital parameters provides a source for detection of many brain related disorders. The dynamic study on EEG signal feature extraction helps to identify the physiological changes induced by human body actions such as mental illness, stress level, emotions and excessive workload. The physiologic condition of an individual need to be assessed and monitored for early detection of brain diseases. The different features of the EEG signal play a key role for detection of many diseases. This paper provides the algorithmic approach for extracting the desired features from an EEG signal and devise the method to identify the EEG frequency from an input raw data. The real time and database signals are considered for evaluating the performance of the proposed method.