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Brain computer interfacing: A spectrum estimation based neurophysiological signal interpretation

Non-invasive process like Electroencephalogram or Electromyogram are much preferred among various Brain-Computer Interfaces techniques. Parametric method for spectral density calculation could be beneficial for such signal due to certain advantages over non-parametric methods like avoiding side-lobe leakages. Auto-regressive and Crosscorrelative analysis of EEG or EMG signals are performed to infer their nature. Nowadays, doctors can prescribe medicines or further investigations simply based on visual analysis of the EEG or EMG graphs, where power spectral density of such signals are assumed stationary after being filtered. This paper proposes a new technique of statistically inferring the health status based on power analysis of neurophysiological signals. Modified Covariance, Burg, and/or Yule-Walker methods are used to calculate spectral densities. Individual weights from such methods are computed to infer a single electrode characteristic. Similar procedure is repeated for each electrode for concluding overall neurophysiological health status. Experimental results thus obtained are quite encouraging.