Single-trial classification of elementary comparison processes on the basis of instantaneous EEG and MEG coherences.

The present study is focused on the evidence of possible single-trial EEG/MEG analysis of information processing. The discrimination between thinking modalities of concept activation and pattern comparison for single tasks of elementary comparison procedures is investigated. A neural network classifier with backpropagation learning algorithm is used.

The sensitivity of instantaneous coherence for considering elementary comparison processing. Part II: Similarities and differences between EEG and MEG coherences.

The EEG (electroencephalogram) coherence depends on EEG deviation type. A high level of sensitivity of instantaneous coherence for investigating elementary cognitive tasks could be shown in the case of unipolar reference (ear lobe reference). In order to validate of this result the same investigations were performed for MEG (magnetoencephalogram) coherence, where EEG and MEG were measured simultaneously.

The sensitivity of instantaneous coherence for considering elementary comparison processing. Part I: The relationship between mental activities and instantaneous EEG coherence.

In this study the sensitivity of instantaneous EEG coherence for the description of the processual evolution of thinking processes is investigated. The adaptive estimation approach presented allows the calculation of the coherence function with high time and frequency resolution. On this basis the detection of time intervals with high synchronization phenomena is possible.

Methods of dynamic spectral analysis by self-exciting autoregressive moving average models and their application to analysing biosignals.

Dynamic methods in the spectral domain are necessary to analyse biological signals because of the frequently nonstationary character of the signals. The paper presents an adaptive procedure of fitting time-dependent ARMA models to nonstationary signals, which is suitable for on-line calculations. The properties of the model parameter estimations are examined, and in the stationary case are compared with the results of convergent estimation methods.