Predicting Age From Brain EEG Signals—A Machine Learning Approach

Background Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. Results We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. Conclusion We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

This report provides a systematic longitudinal analysis of the EEG from infancy into early childhood. Particular emphasis is placed on the empirical confirmation of a 6-9 Hz alpha-range frequency band that has previously been used in the infant EEG literature. EEG data in 1-Hz bins from 3 to 12 Hz were analyzed from a longitudinal sample of 29 participants at 5, 10, 14, 24, and 51 months of age. Inspection of power spectra averaged across the whole sample indicated the emergence of a peak in the 6-9 Hz range across multiple scalp regions. Coding of peaks in the power spectra of individual infants showed a clear developmental increase in the frequency of this peak. A rhythm in the 6-9 Hz emerged at central sites that was independent of the classical alpha rhythm at posterior sites. The relative amplitude of this central rhythm peaked in the second year of life, when major changes are occurring in locomotor behavior. The 6-9 Hz band is a useful alpha-range band from the end of the first year of life into early childhood. The findings also complement other research relating the infant central rhythm with the adult sensorimotor mu rhythm.