In this paper, the trajectories of the poles (radiuses and frequencies) of the time-varying autoregressive (TV-AR) model of the electroencephalogram (EEG) signal are used for monitoring brain injury and recovery. The TV-AR coefficients are obtained using Burg algorithm applied to a sliding window. The norm of the TV-frequencies is suggested to be a quantitative measure for monitoring the brain injury and recovery. The radiuses-frequencies of the TV-poles are displayed as a scattering plot for monitoring and investigating the brain injury and recovery as well. This scattering plot provides more details about injury-related EEG changes. Analysis and results of real-world EEG data illustrate that the norm of the TV-frequencies and the scattering plot of the radiuses-frequencies provide significant tool for investigating and monitoring the brain injury and recovery from the TV-AR model poles. For intensive analysis various model orders are examined. The second-order model introduces itself as the significant one for monitoring the brain injury. With employing the third-order, asphyxia manifests itself by damping the real pole and by increasing the frequencies of the other two poles. In the resuscitation segment the situation moves back and finally in the recovery segment the scattering plot gets closely similar to the normal EEG scattering plot.

This paper proposes an efficient approach for the extraction of the brain evoked potentials (EPs) from a small number of sweeps. In this approach, the ensemble average EP waveforms of different electrodes are computed from a small number of sweeps. Theses EP waveforms are decomposed into uncorrelated components using a correlation-based blind source separation method. By this separation, the evoked potential waveforms could be isolated from the electroencephalogram (EEG), noise and other artifacts waveforms. In order to enhance the signal-to-noise ratio and to recover the desired EP components, each uncorrelated component is filtered through a zero-phase matched filter based on the third-order correlation lags of the filter input. Finally, the evoked potential waveform recorded by every electrode is obtained through the projection of the selected filtered evoked potential components into the electrode space. Experimental results for visual and auditory evoked potentials show that the EP waveforms obtained by applying the proposed approach to 5 sweeps for the visual case and to 2 sweeps for the auditory case are better than those obtained by ensemble averaging and filtering of 45 sweeps.