{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:41:19Z","timestamp":1783161679234,"version":"3.54.6"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center of Innovation Program from Japan Science and Technology Agency (JST), JSPS KAKENHI","award":["25540101"],"award-info":[{"award-number":["25540101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.<\/jats:p>","DOI":"10.3390\/s21051678","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T10:25:18Z","timestamp":1614594318000},"page":"1678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN"],"prefix":"10.3390","volume":"21","author":[{"given":"Panayu","family":"Keelawat","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093-0404, USA"},{"name":"Department of Computer Engineering, Chulalongkorn University, Pathum Wan, Bangkok 10330, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0487-2482","authenticated-orcid":false,"given":"Nattapong","family":"Thammasan","sequence":"additional","affiliation":[{"name":"Human Media Interaction, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Numao","sequence":"additional","affiliation":[{"name":"The Institute of Scientific and Industrial Research, Osaka University, Mihogaoka, Ibaraki, Osaka 567-0047, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boonserm","family":"Kijsirikul","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Chulalongkorn University, Pathum Wan, Bangkok 10330, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118530","DOI":"10.1109\/ACCESS.2019.2936817","article-title":"A hierarchical bidirectional GRU model with attention for EEG-based emotion classification","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Konar, A., and Chakraborty, A. (2015). Emotion Recognition: A Pattern Analysis Approach, Wiley.","DOI":"10.1002\/9781118910566"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Zheng, W.-L., and Lu, B.-L. (2015, January 7\u201312). Cross-subject and cross-gender emotion classification from EEG. Proceedings of the International Federation for Medical and Biological Engineering (IFMBE), Toronto, ON, Canada.","DOI":"10.1007\/978-3-319-19387-8_288"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1093\/cercor\/bht030","article-title":"Spatiotemporal dependency of age-related changes in brain signal variability","volume":"24","author":"McIntosh","year":"2013","journal-title":"Cereb. Cortex"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Al-Nafjan, A., Hosny, M., Al-Ohali, Y., and Al-Wabil, A. (2017). Review and classification of emotion recognition based on EEG brain-computer interface system research: A systematic review. Appl. Sci., 7.","DOI":"10.3390\/app7121239"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/JBHI.2017.2727218","article-title":"Deep belief networks for electroencephalography: A review of recent contributions and future outlooks","volume":"22","author":"Movahedi","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.bbr.2015.10.036","article-title":"Brain functional connectivity patterns for emotional state classification in Parkinson\u2019s disease patients without dementia","volume":"298","author":"Yuvaraj","year":"2016","journal-title":"Behav. Brain Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.3233\/JIFS-169460","article-title":"Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network","volume":"34","author":"Narang","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, J., Meng, H., Nandi, A., and Li, M. (2016, January 13\u201315). Emotion detection from EEG recordings. Proceedings of the International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603437"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fncom.2019.00053","article-title":"Multi-method Fusion of Cross-Subject Emotion Recognition Based on High-Dimensional EEG Features","volume":"13","author":"Yang","year":"2019","journal-title":"Front. Comput. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thammasan, N., Fukui, K., and Numao, M. (2017, January 4\u20139). Multimodal fusion of EEG and musical features in music-emotion recognition. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11112"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, H., Han, J., and Min, K. (2019). A multi-column CNN model for emotion recognition from EEG signals. Sensors, 19.","DOI":"10.3390\/s19214736"},{"key":"ref_14","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_15","unstructured":"Xiang, L., Dawei, S., Peng, Z., Guangliang, Y., Yuexian, H., and Bin, H. (2016, January 15\u201318). Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China."},{"key":"ref_16","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"44317","DOI":"10.1109\/ACCESS.2019.2908285","article-title":"Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., and Bhattacharya, S. (2017, January 6\u20139). Using Deep and Convolutional Neural Networks for accurate emotion classification on DEAP dataset. Proceedings of the Conference on Innovative Applications of Artificial Intelligence (IAAI), San Francisco, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Keelawat, P., Thammasan, N., Kijsirikul, B., and Numao, M. (2019, January 8\u20139). Subject-independent emotion recognition during music listening based on EEG using Deep Convolutional Neural Networks. Proceedings of the IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malaysia.","DOI":"10.1109\/CSPA.2019.8696054"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102172","DOI":"10.1016\/j.bspc.2020.102172","article-title":"Deep learning for motor imagery EEG-based classification: A review","volume":"63","author":"Dawwd","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"68415","DOI":"10.1109\/ACCESS.2019.2919143","article-title":"Universal joint feature extraction for P300 EEG classification using multi-task autoencoder","volume":"7","author":"Ditthapron","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mousavi, S., Afghah, F., and Acharya, U.R. (2019). SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216456"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102094","DOI":"10.1016\/j.bspc.2020.102094","article-title":"Human activity recognition using deep electroencephalography learning","volume":"62","author":"Salehzadeh","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/nn.2726","article-title":"Anatomically distinct dopamine release during anticipation and experience of peak emotion to music","volume":"14","author":"Salimpoor","year":"2011","journal-title":"Nat. Neurosci."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1587\/transinf.2015EDP7251","article-title":"Continuous music-emotion recognition based on electroencephalogram","volume":"E99.D","author":"Thammasan","year":"2016","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_28","unstructured":"Bradley, M.M., and Lang, P.J. (2007). The International Affective Digitized Sounds (IADS-2): Affective Ratings of Sounds and Instruction Manual, University of Florida."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3389\/fnins.2016.00244","article-title":"EEG responses to auditory stimuli for automatic affect recognition","volume":"10","author":"Hettich","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.plrev.2013.05.008","article-title":"From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions","volume":"10","author":"Juslin","year":"2013","journal-title":"Phys. Life Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.3389\/fpsyg.2017.02044","article-title":"Emotional responses to music: Shifts in frontal brain asymmetry mark periods of musical change","volume":"8","author":"Arjmand","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1037\/0033-295X.110.1.145","article-title":"Core affect and the psychological construction of emotion","volume":"110","author":"Russell","year":"2003","journal-title":"Psychol. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1146\/annurev.psych.58.110405.085709","article-title":"The experience of emotion","volume":"58","author":"Barrett","year":"2007","journal-title":"Annu. Rev. Psychol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mariooryad, S., and Busso, C. (2013, January 2\u20133). Analysis and compensation of the reaction lag of evaluators in continuous emotional annotations. Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII), Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.21"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Candra, H., Candra, H., Yuwono, M., Chai, R., Handojoseno, A., Elamvazuthi, I., Nguyen, H.T., and Su, S. (2015, January 25\u201329). Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320065"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2013.06.046","article-title":"Emotional state classification from EEG data using machine learning approach","volume":"129","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_37","unstructured":"LeCun, Y., Kavukcuoglu, K., and Farabet, C. (June, January 30). Convolutional networks and applications in vision. Proceedings of the International Symposium on Circuits and Systems (ISCAS), Paris, France."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wen, Z., Xu, R., and Du, J. (2017, January 15\u201317). A novel convolutional neural network for emotion recognition based on EEG signal. Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304360"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"509","DOI":"10.3233\/THC-174836","article-title":"Emotion recognition from multichannel EEG signals using K-nearest neighbor classification","volume":"26","author":"Li","year":"2018","journal-title":"Technol. Health Care"},{"key":"ref_40","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 (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461315"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cheng, L., Leung, A., and Ozawa, S. (2018). Continuous convolutional neural network with 3d input for EEG-based emotion recognition. Neural Information Processing, Proceedings of the International Conference on Neural Information Processing (ICONIP), Siem Reap, Cambodia, 13\u201316 December 2018, Springer.","DOI":"10.1007\/978-3-030-04221-9"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cho, J., and Hwang, H. (2020). Spatio-temporal representation of an electroencephalogram for emotion recognition using a three-dimensional convolutional neural network. Sensors, 20.","DOI":"10.3390\/s20123491"},{"key":"ref_44","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","unstructured":"(2021, January 15). CNN Implementation for EEG-Emotion Recognition during Music Listening. Available online: https:\/\/github.com\/Gpanayu\/EmoRecogKeras."},{"key":"ref_46","unstructured":"Kim, Y., Schmidt, E., and Emelle, L. (2008, January 14\u201318). Moodswings: A collaborative game for music mood label collection. Proceedings of the International Conference on Music Information Retrieval (ISMIR), Philadelphia, PA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, Y.-H., and Chen, H.H. (2011). Music Emotion Recognition, CRC Press. [1st ed.].","DOI":"10.1201\/b10731"},{"key":"ref_48","unstructured":"(2021, January 01). Java Sound Technology. Available online: https:\/\/docs.oracle.com\/javase\/7\/docs\/technotes\/guides\/sound."},{"key":"ref_49","unstructured":"(2021, January 19). waveguard\u2122 EEG caps. Available online: https:\/\/www.ant-neuro.com\/products\/waveguard_caps."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/nrn3666","article-title":"Brain correlates of music-evoked emotions","volume":"15","author":"Koelsch","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_51","unstructured":"(2021, January 19). TEAC CORPORATION: International Website. Available online: https:\/\/www.teac.co.jp\/int."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1155\/2011\/130714","article-title":"EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG processing","volume":"2011","author":"Delorme","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1111\/1469-8986.3720163","article-title":"Removing electroencephalographic artifacts by blind source separation","volume":"37","author":"Jung","year":"2000","journal-title":"Psychophysiology"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximization approach to blind separation and blind deconvolution","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"156869","DOI":"10.1155\/2011\/156869","article-title":"FieldTrip: Open-source software for advanced analysis of MEG, EEG, and invasive electrophysiological data","volume":"2011","author":"Oostenveld","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Thammasan, N., and Miyakoshi, M. (2020). Cross-Frequency Power-Power Coupling Analysis: A useful cross-frequency measure to classify ICA-decomposed EEG. Sensors, 20.","DOI":"10.3390\/s20247040"},{"key":"ref_57","first-page":"226","article-title":"Knowledge matters: Importance of prior information for optimization","volume":"17","author":"Bengio","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"8875426","DOI":"10.1155\/2020\/8875426","article-title":"EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities","volume":"2020","author":"Suhaimi","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta Protein Struct."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W., and Boots, R. (2018, January 2\u20137). Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11496"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/1467-9868.00346","article-title":"A direct approach to false discovery rates","volume":"64","author":"Storey","year":"2002","journal-title":"J. Royal Stat. Soc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Mental Develop."},{"key":"ref_63","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. Affective Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"107003","DOI":"10.1016\/j.measurement.2019.107003","article-title":"Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals","volume":"150","author":"Lu","year":"2020","journal-title":"Measurement"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_66","unstructured":"Pandey, P., and Seeja, K.R. (2019). Subject independent emotion recognition from EEG using VMD and deep learning. J. King Saud Univ. Comp. Info. Sci., 53\u201358."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TCDS.2018.2826840","article-title":"Domain adaptation techniques for EEG-based emotion recognition: A comparative study on two public datasets","volume":"11","author":"Lan","year":"2019","journal-title":"IEEE Trans. Cogn. Devel. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TCYB.2016.2633306","article-title":"Learning domain-invariant subspace using domain features and independence maximization","volume":"48","author":"Yan","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1111\/1469-8986.00121","article-title":"Clarifying the emotive functions of asymmetrical frontal cortical activity","volume":"40","year":"2003","journal-title":"Psychophysiology"},{"key":"ref_71","first-page":"720","article-title":"Review of EEG, ERP, and brain connectivity estimators as predictive biomarkers of social anxiety disorder","volume":"11","author":"Abdulhakim","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3389\/neuro.09.061.2009","article-title":"High-frequency broadband modulation of electroencephalographic spectra","volume":"3","author":"Onton","year":"2009","journal-title":"Front. Hum. Neurosci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TNSRE.2015.2508759","article-title":"Real-time adaptive EEG source separation using online recursive independent component analysis","volume":"24","author":"Hsu","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1109\/TBME.2015.2481482","article-title":"Real-time neuroimaging and cognitive monitoring using wearable dry EEG","volume":"62","author":"Mullen","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Plechawska-Wojcik, M., Kaczorowska, M., and Zapa\u0142a, D. (2018, January 16\u201318). The artifact subspace reconstruction (ASR) for EEG signal correction. A comparative study. Proceedings of the International Conference Information Systems Architecture and Technology (ISAT), Nysa, Poland.","DOI":"10.1007\/978-3-319-99996-8_12"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:30:48Z","timestamp":1760160648000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,1]]},"references-count":75,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051678"],"URL":"https:\/\/doi.org\/10.3390\/s21051678","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,1]]}}}