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 first 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 nonperiodic 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. We next 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.