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The paper presents the detection of the state the human being is in by analyzing the EEG signals of the brain. Different states of human being are waking state,dreaming state and deep sleep state.as these states change the frequency of the brain waves which are represented by EEG signal changes. The EEG is generally divided into four different types of waveforms with respect to their frequencies Delta (0 to5-3) Hz, Theta (4 to-7)Hz, Alpha(8 to-13)Hz and Beta(14 to 30)Hz. These four waveforms are basic waveforms of EEG.The EEG signals are recorded by placing electrodes on brain and can extract by using the LABVIEW.with the help of DAQ board the EEG signals can be analysed by using LABVIEW software. 

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We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.

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Electroencephalography (EEG) offers a non-invasive brain-imaging technology with potential to extract user intent from brain signals. This can offer a potential method for dispersed soldiers to communicate silently with one another. The usual interface for acquiring EEG signals may house 128 or more electrodes. Each EEG signal may be sampled at KHz sampling rates and may last for a few seconds. Thus the number of samples used to represent each trial can be large. The goal of this short-term innovative research (STIR) project was to investigate innovative sample and channel (i.e., EEG electrode) selection methods to reduce the storage and computational complexity in analyzing EEG signals. In experiments aimed at determining the redundancy in imagined speech EEG signals, it was observed that EEG data has limited spatial redundancy, but large temporal redundancy. In another set of experiments, we investigated the classification of two imagined speech syllables (namely Ba and Ku) from imagined speech EEG signals. Using all good channels, only one of the seven volunteer subjects produced better than chance classification accuracy of about 60%. By selecting specific electrodes, two subjects yielded better-than-chance results with recognition rates close to 60% for all trials. Overall classification rates appear to have improved slightly by the selection of electrodes, indicating that imagined speech classification performance can be improved by careful selection of EEG electrodes.