A study of pattern recognition in children using single-channel electroencephalogram for specialized electroencephalographic devices

In this paper, we aim to classify two classes in children by using single-channel electroencephalogram (EEG). EEG has been used to define neural patterns and to adjust the wide applicability to a larger population of healthy and diseased users. Specialized EEG devices have recently developed as for compact and portable measurement system using them in the real environment. If there is a multiplex state estimation system with EEG through a specialized EEG device, it would be a powerful tool for neuroscience studies and clinical applications. We firstly focused on the state of concentration; therefore, two kinds of single-channel EEG signals (during meditation and concentration) from 10 children were measured. Recordings were processed to remove artifacts, and then extracted their periodic or non-periodic features by three methods (Fourier transform, wavelet transform, and empirical mode decomposition). Elastic net logistic regression constructed predictive models to classify two classes of the optimized extracted features. A model showed 0.988 area under the receiver operating characteristic curve when wavelet transform was selected as feature extraction method. Our next is to construct a multiplex state estimation system. Finally, we will make portable applications using a specialized EEG device that include the multiplex model and encourage children to develop the child's sense.