Identification and labeling of EEG graphic elements using autoregressive spectral estimates.
Syntactic EEG analysis requires descriptive labeling of short (1 s) epochs in an EEG. While discriminant analysis methods are useful for this purpose, significant improvement in label correctness can be achieved using the heuristic method described in this paper. The method is based on the estimation of frequency spectra by autoregressive (AR) modeling. The location of the peak frequencies and the power of these peaks are used to assign labels to 1 s epochs. Artefacts and epochs with exceptionally high or low amplitude and/or frequency values are identified as well. The assignment of labels is accomplished by comparing parameters, extracted from the power spectra estimated for 1 s epochs, with thresholds. These thresholds are automatically adapted to each individual EEG lead. In this paper, the method is outlined and its performance is compared with a discriminant analysis approach and visual labeling.
D003201:Computers; D004569:Electroencephalography; D006801:Humans; D010363:Pattern Recognition, Automated; D012044:Regression Analysis