Abstract
This study investigates the EEG signals obtained from Children with Attention deficit hyperactivity disorder (ADHD) and typically developing (TD) children while performing a hybrid Simon–spatial Stroop task, which is aimed to achieve a high classification rate. First, a subset EEG channels were selected using principal component analysis (PCA) to preserve as much information as the full set of 128 channels. Second, the feature set consisted of the time-domain amplitude in all the segmentation time windows from 30 subjects with leave-one-out (LOO) cross-validation strategy, which was collected from the optimal channels in prefrontal cortex and inferior parietal area during four different conditions. Then, K-nearest neighbors (K-NN) and support vector machine (SVM) were used to classify ADHD and TD. The results showed that the best classification accuracy of 83.33 % was achieved by K-NN classifier, suggesting that the method could detect ADHD effectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hauser, M.E.: Prediction of stimulant response in patients with ADHD utilizing acute medication challenge studies Ph.D. thesis, UMI, Published by Pro Quest LLC (2013)
Liu, X., Banich, M.T.: Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI. NeuroImage 22, 1097–1106 (2004)
He, L., Hu, Y.: Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG. Neurocomputing 121, 423–433 (2013)
Arvaneh, M.: Optimizing the channel selection and classification accuracy in EEG-Based BCI. IEEE Trans. Biomed. Eng. 58(6), (2011)
Zou, L. Hui, P.: Analysis of attention deficit hyperactivity disorder and control participants in EEG using ICA and PCA. ISNN, (2012) Part I, LNCS 7367, 403–410 (2012)
Poil, S.-S., Bollmann, S.: Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clin. Neurophysio. 125, 1626–1638 (2014)
Allahverdy, A., Nasrabadi. A.M.: Detecting ADHD children using symbolic dynamic of nonlinear features of EEG. Electrical Engineering (ICEE), 19th Iranian Conference (2011)
Ghassemi, F.: Using non-linear features of EEG for ADHD/normal participants’ classification. Soc. Behav. Sci. 32, 148–152 (2012)
Cao, J., Suhong, W.: Interference control in 6–11 year-old children with and without ADHD: behavioral and ERP study. Dev. Neurosci. 31, 342–349 (2013)
Acknowledgments
This work has been partially supported by National Natural Science Foundation of China (61201096, 51307010, 81101018), University Natural Science Research Program of Jiangsu Province (13KJB510002), the Science and Technology Program of Changzhou City (CE20145055, CE20135060, CJ20130026), and Qing Lan Project of Jiangsu Province.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Yang, J., Li, W., Wang, S., Lu, J., Zou, L. (2016). Classification of Children with Attention Deficit Hyperactivity Disorder Using PCA and K-Nearest Neighbors During Interference Control Task. In: Wang, R., Pan, X. (eds) Advances in Cognitive Neurodynamics (V). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0207-6_61
Download citation
DOI: https://doi.org/10.1007/978-981-10-0207-6_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0205-2
Online ISBN: 978-981-10-0207-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)