{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T18:10:23Z","timestamp":1735495823375,"version":"3.32.0"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018R1A2B2005687"],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & communications Technology Planning & Evaluation","award":["2017-0-00451","2019-0-01842"]},{"DOI":"10.13039\/501100003706","name":"Korea Research Institute of Standards and Science (KRISS)","doi-asserted-by":"crossref","award":["2019 \u2013 GP2019-0018"],"id":[{"id":"10.13039\/501100003706","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach\u2019s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.<\/jats:p>","DOI":"10.3390\/s19235317","type":"journal-article","created":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T09:30:35Z","timestamp":1575451835000},"page":"5317","source":"Crossref","is-referenced-by-count":27,"title":["Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network\u2014Feasibility Study"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5452-166X","authenticated-orcid":false,"given":"Moonyoung","family":"Kwon","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea"}]},{"given":"Sangjun","family":"Han","sequence":"additional","affiliation":[{"name":"AI Core Development Team, LG Electronics, Seoul 07796, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1195-5681","authenticated-orcid":false,"given":"Kiwoong","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Biosignals, Korea Research institute of Science and Standards, Daejeon 34113, Korea"},{"name":"Department of Medical Physics, University of Science and Technology, Daejeon 34113, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-4436","authenticated-orcid":false,"given":"Sung Chan","family":"Jun","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, L.M. (2016, January 27\u201330). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201323). Residual dense network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q., and Maaten, L. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_9","unstructured":"Kuleshov, V., Enam, S.Z., and Ermon, S. (2017). Audio super-resolution using neural nets. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2015.08.015","article-title":"EEG source localization: Sensor density and head surface coverage","volume":"256","author":"Song","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S1388-2457(02)00337-1","article-title":"Epileptic source localization with high density EEG: How many electrodes are needed?","volume":"114","author":"Lantza","year":"2003","journal-title":"Clin. Neurophysiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3758\/BF03209412","article-title":"Estimating the spatial Nyquist of the human EEG","volume":"31","author":"Srinivasan","year":"1998","journal-title":"Behav. Res. Methods Instrum. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/0013-4694(93)90121-B","article-title":"Spatial sampling of head electrical fields: The geodesic sensor net","volume":"87","author":"Tucker","year":"1993","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.1152\/jn.00105.2010","article-title":"Removal of movement artifact from high-density EEG recorded during walking and running","volume":"103","author":"Gwin","year":"2010","journal-title":"J. Neurophysiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/0013-4694(75)90056-5","article-title":"An on-line transformation of EEG scalp potentials into orthogonal source derivations","volume":"39","author":"Hjorth","year":"1975","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/0013-4694(83)90040-8","article-title":"On-line source-density computation with a minimum of electrodes","volume":"56","author":"MacKay","year":"1983","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/S1388-2457(01)00494-1","article-title":"Spatial enhancement of EEG data by surface Laplacian estimation: The use of magnetic resonance imaging-based head models","volume":"112","author":"Babiloni","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0013-4694(87)90141-6","article-title":"Mapping of scalp potentials by surface spline interpolation","volume":"66","author":"Perrin","year":"1987","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_19","unstructured":"Nunez, P.L. (1995). Neocortical Dynamics and Human EEG Rhythms, Oxford University Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/BF01186911","article-title":"Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials","volume":"8","author":"Srinivasan","year":"1996","journal-title":"Brain Topogr."},{"key":"ref_21","unstructured":"Ferree, T.C., and Srinivasan, R. (2000). Theory and Calculation of the Scalp Surface Laplacian, Electrical Geodesics, Inc."},{"key":"ref_22","unstructured":"Nunez, P.L. (1981). Electric Fields of the Brain, Oxford University Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/BF01128862","article-title":"Beyond topographic mapping: Towards functional-anatomical imaging with 124-channel EEGs and 3-D MRIs","volume":"3","author":"Gevins","year":"1990","journal-title":"Brain Topogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1016\/j.clinph.2012.06.005","article-title":"Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions","volume":"123","author":"Tenke","year":"2012","journal-title":"Clin. Neurophysiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.clinph.2005.08.034","article-title":"Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks","volume":"117","author":"Kayser","year":"2006","journal-title":"Clin. Neurophysiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/BF01128691","article-title":"Comparison of high resolution EEG methods having different theoretical bases","volume":"5","author":"Nunez","year":"1993","journal-title":"Brain Topogr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ijpsycho.2015.05.004","article-title":"Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view","volume":"97","author":"Burle","year":"2015","journal-title":"Int. J. Psychophysiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aviyente, S. (2007, January 26\u201329). Compressed Sensing Framework for EEG Compression. Proceedings of the IEEE\/SP 14th Workshop on Statistical Signal Processing, Madison, WI, USA.","DOI":"10.1109\/SSP.2007.4301243"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TBME.2012.2217959","article-title":"Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware","volume":"64","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","first-page":"Y1","article-title":"Monitoring and diagnosis of Alzheimer\u2019s disease using noninvasive compressive sensing EEG","volume":"8750","author":"Morabito","year":"2013","journal-title":"Proc. SPIE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3255","DOI":"10.1109\/JSEN.2013.2263794","article-title":"Enhanced compressibility of EEG signal in Alzheimer\u2019s disease patients","volume":"13","author":"Morabito","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Corley, I.A., and Huang, Y. (2018, January 4\u20137). Deep EEG super-resolution: Upsampling EEG spatial resolution with generative adversarial networks. Proceedings of the IEEE EMBS International Conference on Biomedical & Health Information, Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333379"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, S., Kwon, M., Lee, S., and Jun, S.C. (2018, January 7\u201310). Feasibility study of EEG super-resolution using deep convolutional networks. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00184"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1088\/0031-9155\/40\/3\/001","article-title":"A fast method to comput surface potentials generated by dipoles within multilayer anisotropic spheres","volume":"40","author":"Zhang","year":"1995","journal-title":"Phys. Med. Biol."},{"key":"ref_35","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representaitons in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_37","first-page":"23915","article-title":"Deconvolution and checkerboard artifacts","volume":"10","author":"Odena","year":"2016","journal-title":"Distill"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interface","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TPAMI.2010.125","article-title":"Convolutional neural networks for P300 detection with application to brain\u2013computer interfaces","volume":"33","author":"Cecotti","year":"2011","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gupta, K., and Majumdar, A. (2016, January 24\u201329). Sparsely connected autoencder. Proceedings of the International Joint Conference on Neural Networks, Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727437"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. Proceedings of the IEEE Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_43","unstructured":"Kingma, P., and Ba, J. (2015). ADAM: A method for stochastic optimization. arXiv."},{"key":"ref_44","unstructured":"Sekihara, K., and Nagarajan, S.S. (2008). Adaptive Spatial Filters for Electromagnetic Brain Imaging, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stropahl, M., Bauer, A.K.R., Debener, S., and Bleichner, M.G. (2018). Source-modeling auditory processes of EEG data using EEGLAB and Brainstorm. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00309"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/0013-4694(78)90029-9","article-title":"Age-related variations in evoked potentials to auditory stimuli in normal human subjects","volume":"44","author":"Goodin","year":"1978","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"724","DOI":"10.3389\/fpsyg.2014.00742","article-title":"Beat-induced fluctuations in auditory cortical beta-band activity: Using EEG to measure age-related changes","volume":"5","author":"Cirelli","year":"2014","journal-title":"Front. Psychol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wen, Z., Xu, R., and Du, J. (2017, January 15\u201317). A novel convolutional neural networks for emotion recognition based on EEG signal. Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics, Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304360"},{"key":"ref_49","unstructured":"Looney, D., Park, C., Kidmose, P., Rank, M.L., Ungstrup, M., Rosenkranz, K., and Mandic, D.P. (September, January 30). An in-the-ear platform for recording electroencephalogram. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnins.2015.00438","article-title":"EEG recorded from the Ear: Characterizing the Ear-EEG method","volume":"9","author":"Mikkelsen","year":"2015","journal-title":"Front. Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s12938-017-0400-5","article-title":"Automatic sleep staging using ear-EEG","volume":"16","author":"Mikkelsen","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Nakamura, T., Alqurashi, Y.D., Morrell, M.J., and Mandic, D.P. (2018, January 8\u201313). Automatic detection of drowsiness using in-ear EEG. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489723"},{"key":"ref_53","first-page":"169","article-title":"Processing and spectral analysis of the raw EEG signal from the MindWave","volume":"90","year":"2014","journal-title":"Prz. Elektrotechniczny"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnins.2017.00109","article-title":"Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research","volume":"11","author":"Krigolson","year":"2017","journal-title":"Front. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T17:56:37Z","timestamp":1735494997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,3]]},"references-count":54,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235317"],"URL":"https:\/\/doi.org\/10.3390\/s19235317","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,12,3]]}}}