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.

In this paper, a segment of the electroencephalogram (EEG) signal of a subject is viewed as outcomes of a single and a joint two random variables. The Shannon entropy (self information) contained in the single random variable and the mutual information associated with the joint two random variables can be used for the diagnosis of Alzheimer’s disease. It is shown that the self information may provide diagnostic error while the mutual information provides accurate and significant diagnosis. This can be justified by the following fact. The mutual information between a current sample and a delayed one of the EEG signal is a quantitative measure for the information of the current sample contained in the past one. Thus, this mutual information is related to the subject memory, which experiences a problem in Alzheimer’s disease.

In this paper, an adaptive approach using the least-mean-square lattice (LMSL) is proposed for the segmentation and tracking of the electro-encephalogram (EEG) signal. In the proposed approach, the time-trajectories of the reflection coefficients of the adaptive lattice predictor as well as the on-line power spectrum estimate are used as classification, segmentation and tracking parameters. The adaptive lattice predictor consists of cascaded similar first-order sections. Theses sections are independent due to the orthogonality principle linked to the least-mean square (LMS) algorithm. Therefore, on-line adding a new section makes no influence on the values of the coefficients of the preceding sections. Such property of the adaptive lattice predictor is not valid when the direct-form linear prediction filter is employed. Further advantage of the lattice predictor is the fast convergence due to independence of the successive sections, which yields no matrix computation. Results for tracking sleep spindles of computer-generated and real-world EEG data are presented to show the significant usefulness of the proposed approach. It is shown that an adaptive lattice predictors consisting of three up to four sections are satisfactory for the detection and tracking of the sleep spindles. A short-time (i.e., sliding window) implementation of Burg’s method (STBM) for computing the reflection coefficients of the lattice predictor is also suggested and examined. This method shows that the time-trajectories of the lattice coefficients, the on-line power spectrum and a proposed quantitative measure are capable of distinguishing between the EEG from normal healthy subject and the EEG from Alzheimer patient subject.

A quantitative electroencephalogram (qEEG) index for the evaluation and monitoring of cortical health in ischemic brain injury is presented. The proposed qEEG index, called the subbands spectral complexity distance (SSCD), measures the distance between a vector of subbands spectral complexities computed from the investigated EEG and a one computed from normal EEG. For the computation of the SSCD, the spontaneous power spectral density of the EEG signal is estimated using the time-varying autoregressive (TV-AR) model, decomposed into subbands and the spectral complexity of each subband is computed. Results of evaluating human EEG data are presented to show the usefulness of the proposed SSCD index. It is shown that the index increases during the time-segments of ischemic brain injury while decreases during the normal and recovery segments. This can be explained by the fact that the ischemic brain injury may increase the irregularity of the EEG signal and therefore the power spectral density becomes more complex with ischemic brain injury.