{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T10:56:51Z","timestamp":1784977011268,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:00:00Z","timestamp":1572480000000},"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":["2017R1C1B5017918"],"award-info":[{"award-number":["2017R1C1B5017918"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1D1A1A02050292"],"award-info":[{"award-number":["NRF-2018R1D1A1A02050292"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.<\/jats:p>","DOI":"10.3390\/s19214736","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T06:33:29Z","timestamp":1572503609000},"page":"4736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":160,"title":["A Multi-Column CNN Model for Emotion Recognition from EEG Signals"],"prefix":"10.3390","volume":"19","author":[{"given":"Heekyung","family":"Yang","sequence":"first","affiliation":[{"name":"Industry-Academy Cooperation Foundation, Sangmyung University, Seoul 03016, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongdae","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sangmyung University, Seoul 03016, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungha","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sangmyung University, Seoul 03016, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.patrec.2007.01.002","article-title":"Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis","volume":"28","author":"Brunner","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","article-title":"Emotion recognition from EEG using higher order crossings","volume":"14","author":"Petrantonakis","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Korats, G., Le Cam, S., Ranta, R., and Hamid, M. (2012, January 1\u20134). Applying ICA in EEG: choice of the window length and of the decorrelation method. Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies, Vilamoura, Algarve, Portugal.","DOI":"10.1007\/978-3-642-38256-7_18"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., and Lu, B.L. (2013, January 6\u20138). Differential entropy feature for EEG-based emotion classification. Proceedings of the IEEE Conference on Neural Engineering, San Diego, CA, USA.","DOI":"10.1109\/NER.2013.6695876"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature extraction and selection for emotion recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TCDS.2016.2587290","article-title":"Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis","volume":"9","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Cognit. Dev. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10044-016-0567-6","article-title":"Emotion recognition from EEG signals by using multivariate empirical mode decomposition","volume":"21","author":"Mert","year":"2018","journal-title":"Pattern Anal. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"627892","DOI":"10.1155\/2014\/627892","article-title":"EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation","volume":"2014","author":"Jirayucharoensak","year":"2014","journal-title":"Sci. World J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TNNLS.2013.2280271","article-title":"ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal","volume":"25","author":"Khosrowabadi","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","first-page":"355","article-title":"Emotion recognition based on EEG using LSTM recurrent neural network","volume":"8","author":"Alhagry","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., and Bhattacharya, S. (2017, January 4\u20139). Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. Proceedings of the AAAI Conference on Innovative Applications, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"ref_12","first-page":"329","article-title":"EEG-based emotion recognition using 3D convolutional neural networks","volume":"9","author":"Salama","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., and Chen, X. (2018, January 8\u201313). Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. Proceedings of the International Joint Conference on Neural Networks, Rio, Brasil.","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Moon, S.-E., Jang, S., and Lee, J.-S. (2018, January 15\u201320). Convolutional neural network approach for EEG-based emotion recognition using brain connectivity and its spatial information. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461315"},{"key":"ref_15","first-page":"310","article-title":"Emotion Recognition from EEG Using RASM and LSTM","volume":"819","author":"Li","year":"2018","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3389\/fnbot.2019.00037","article-title":"SAE+LSTM: A New framework for emotion recognition from multi-channel EEG","volume":"13","author":"Xing","year":"2019","journal-title":"Front. Nuerorobot."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Scovanner, P., Ali, S., and Shah, M. (2007, January 24\u201329). A 3-dimensional SIFT descriptor and its application to action recognition. Proceedings of the ACM International Conference on Multimedia, Augsburg, Germany.","DOI":"10.1145\/1291233.1291311"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Klaser, A., Marszaek, M., and Schmid, C. (2008, January 1\u20134). A spatio-temporal descriptor based on 3D-gradients. Proceedings of the British Machine Vision Conference, Leeds, UK.","DOI":"10.5244\/C.22.99"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, M., Shan, S., Wang, R., and Chen, X. (2014, January 24\u201327). Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.226"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TAFFC.2015.2436926","article-title":"Analysis of EEG signals and facial expressions for continuous emotion detection","volume":"7","author":"Soleymani","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TCYB.2017.2788081","article-title":"Spatial-temporal recurrent neural network for emotion recognition","volume":"49","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ciresan, D., Meier, U., and Schmidhuber, J. (2012, January 18\u201320). Multi-column deep neural networks for image classification. Proceedings of the IEEE Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis; Using Physiological Signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1016\/j.compbiomed.2013.10.017","article-title":"EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm","volume":"43","author":"Yoon","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Naser, D.S., and Saha, G. (2013, January 28\u201330). Recognition of emotions induced by music videos using DT-CWPT. Proceedings of the Indian Conference on Medical Informatics and Telemedicine, Kharagpur, India.","DOI":"10.1109\/IndianCMIT.2013.6529408"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rozgic, V., Vitaladevuni, S.N., and Prasad, R. (2013, January 26\u201331). Robust the EEG emotion classification using segment level decision fusion. Proceedings of the IEEE Conference of Acoustics, Speech, and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6637858"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhuang, X., Rozgic, V., and Crystal, M. (2014, January 1\u20134). Compact unsupervised EEG response representation for emotion recognition. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864469"},{"key":"ref_28","unstructured":"Chen, J., Hu, B., Xu, L., Moore, P., and Su, Y. (2015, January 9\u201312). Feature-level fusion of multimodal physiological signals for emotion recognition. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, Washington, DC, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2015.10.049","article-title":"Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers","volume":"47","author":"Atkinson","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2017.03.027","article-title":"Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals","volume":"244","author":"Ramzan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., and Hu, B. (2017, January 13\u201316). Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MI, USA.","DOI":"10.1109\/BIBM.2016.7822545"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:30:48Z","timestamp":1760189448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,31]]},"references-count":31,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19214736"],"URL":"https:\/\/doi.org\/10.3390\/s19214736","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,31]]}}}