{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T05:40:07Z","timestamp":1746769207331,"version":"3.40.5"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031876561","type":"print"},{"value":"9783031876578","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-87657-8_15","type":"book-chapter","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T05:03:56Z","timestamp":1746767036000},"page":"209-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Pipeline for EEG Classification: Evaluating Models, Preprocessing and Subject Generalizability"],"prefix":"10.1007","author":[{"given":"Deepak","family":"Mewada","sequence":"first","affiliation":[]},{"given":"Mohammed Fayiz","family":"Parappan","sequence":"additional","affiliation":[]},{"given":"Debasis","family":"Samanta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"issue":"6","key":"15_CR1","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","volume":"113","author":"JR Wolpaw","year":"2002","unstructured":"Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767\u2013791 (2002)","journal-title":"Clin. Neurophysiol."},{"issue":"7099","key":"15_CR2","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature04968","volume":"442","author":"G Santhanam","year":"2006","unstructured":"Santhanam, G., Ryu, S.I., Yu, B.M., Afshar, A., Shenoy, K.V.: A high-performance brain-computer interface. Nature 442(7099), 195\u2013198 (2006)","journal-title":"Nature"},{"key":"15_CR3","unstructured":"Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Hoboken, NJ, USA (2013)"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1016\/j.ins.2007.11.012","volume":"178","author":"S-M Zhou","year":"2008","unstructured":"Zhou, S.-M., Gan, J.Q., Sepulveda, F.: Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf. Sci. 178, 1629\u20131640 (2008)","journal-title":"Inf. Sci."},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Deep learning methods for EEG neural classification. In: Handbook of Neuroengineering (2023). https:\/\/doi.org\/10.1007\/978-981-16-5540-1_78","DOI":"10.1007\/978-981-16-5540-1_78"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez-Pozo, N. N.: Deep learning for automatic electroencephalographic signals classification. https:\/\/doi.org\/10.1007\/978-3-031-34953-9_20 (2023)","DOI":"10.1007\/978-3-031-34953-9_20"},{"key":"15_CR7","unstructured":"Pandey, D., Namdeo, V.: Motor imagery feature extraction cum optimization for detection of ALS disease. Solid State Technol. (2021)"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Deep Learning in EEG: Advance of the last ten-year critical period. IEEE Trans. Cogn. Dev. Syst. (2022). https:\/\/doi.org\/10.1109\/tcds.2021.3079712","DOI":"10.1109\/tcds.2021.3079712"},{"key":"15_CR9","doi-asserted-by":"publisher","unstructured":"A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals (2022). https:\/\/doi.org\/10.1016\/b978-0-12-824054-0.00009-5","DOI":"10.1016\/b978-0-12-824054-0.00009-5"},{"issue":"5","key":"15_CR10","doi-asserted-by":"publisher","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","volume":"16","author":"Y Roy","year":"2019","unstructured":"Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019). https:\/\/doi.org\/10.1088\/1741-2552\/ab260c","journal-title":"J. Neural Eng."},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"102738","DOI":"10.1016\/j.artmed.2023.102738","volume":"147","author":"X Wang","year":"2024","unstructured":"Wang, X., Liesaputra, V., Liu, Z., Wang, Y., Huang, Z.: An in-depth survey on deep learning-based motor imagery electroencephalogram (EEG) classification. Artif. Intell. Med. 147, 102738 (2024). https:\/\/doi.org\/10.1016\/j.artmed.2023.102738","journal-title":"Artif. Intell. Med."},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Iftikhar, M., Khan, S. A., Hassan, A.: A survey of deep learning and traditional approaches for EEG signal processing and classification. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 395\u2013400. IEEE (2018). https:\/\/doi.org\/10.1109\/IEMCON.2018.8614893","DOI":"10.1109\/IEMCON.2018.8614893"},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Robinson, N., Lee, S.-W., Guan, C.: EEG representation in deep convolutional neural networks for classification of motor imagery. In: Systems, Man and Cybernetics, pp. 8914184. IEEE (2019). https:\/\/doi.org\/10.1109\/SMC.2019.8914184","DOI":"10.1109\/SMC.2019.8914184"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Sun, M., Sclabassi, R. J.: Optimal selection of the sampling rate for efficient EEG data acquisition. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 804046. IEEE (1999). https:\/\/doi.org\/10.1109\/IEMBS.1999.804046","DOI":"10.1109\/IEMBS.1999.804046"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Jing, H., Takigawa, M.: Low sampling rate induces high correlation dimension on electroencephalograms from healthy subjects. Psychiatry Clin. Neurosci. (2000). https:\/\/doi.org\/10.1046\/J.1440-1819.2000.00729.X","DOI":"10.1046\/J.1440-1819.2000.00729.X"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Heilmeyer, F.A., Schirrmeister, R.T., Fiederer, L.D.J., V\u00f6lker, M., Behncke, J., Ball, T.: A large-scale evaluation framework for EEG deep learning architectures. In: Systems, Man and Cybernetics, pp. 00185. IEEE (2018). https:\/\/doi.org\/10.1109\/SMC.2018.00185","DOI":"10.1109\/SMC.2018.00185"},{"key":"15_CR17","unstructured":"Anshul, Bansal, D., Mahajan, R.: Design and implementation of efficient digital filter for preprocessing of EEG Signals. In: International Conference on Computing for Sustainable Global Development. (2019)"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Suveetha Dhanaselvam, P., Nadia Chellam, C.: A Review on preprocessing of EEG signal. In: 2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1\u20137. IEEE (2023). https:\/\/doi.org\/10.1109\/ICBSII58188.2023.10181071","DOI":"10.1109\/ICBSII58188.2023.10181071"},{"issue":"1","key":"15_CR19","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.bbe.2022.12.007","volume":"43","author":"J Zheng","year":"2023","unstructured":"Zheng, J., et al.: Effects of sampling rate on multiscale entropy of electroencephalogram time series. Biocybernetics Biomed. Eng. 43(1), 233\u2013245 (2023). https:\/\/doi.org\/10.1016\/j.bbe.2022.12.007","journal-title":"Biocybernetics Biomed. Eng."},{"key":"15_CR20","doi-asserted-by":"publisher","unstructured":"Jaw, F.-S.: Optimal sampling of electrophysiological signals. Neurosci. Res. Commun. (2001). https:\/\/doi.org\/10.1002\/NRC.1007","DOI":"10.1002\/NRC.1007"},{"key":"15_CR21","doi-asserted-by":"publisher","unstructured":"Sol\u00f3rzano-Esp\u00edndola, C. E., Sossa, H., Sossa, H., Zamora, E.: A comparison study of EEG signals classifiers for inter-subject generalization. In: [Title of the Book or Conference], pp. 29 (2021). https:\/\/doi.org\/10.1007\/978-3-030-77004-4_29","DOI":"10.1007\/978-3-030-77004-4_29"},{"key":"15_CR22","doi-asserted-by":"publisher","unstructured":"Huang, G., et al.: Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives. Front. Neurosci. (2023). https:\/\/doi.org\/10.3389\/fnins.2023.1122661","DOI":"10.3389\/fnins.2023.1122661"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"96821","DOI":"10.1109\/ACCESS.2022.3205734","volume":"10","author":"RC Maswanganyi","year":"2022","unstructured":"Maswanganyi, R.C., Tu, C., Owolawi, P.A., Du, S.: Statistical evaluation of factors influencing inter-session and inter-subject variability in EEG-based brain computer interface. IEEE Access 10, 96821\u201396839 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3205734","journal-title":"IEEE Access"},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Saha, S., Baumert, M.: Intra- and inter-subject variability in EEG-based sensorimotor brain computer interface: a review. Frontiers Comput. Neurosci. 13 (2020). https:\/\/doi.org\/10.3389\/fncom.2019.00087, https:\/\/www.frontiersin.org\/journals\/computational-neuroscience\/articles\/10.3389\/fncom.2019.00087","DOI":"10.3389\/fncom.2019.00087"},{"key":"15_CR25","doi-asserted-by":"publisher","unstructured":"Brunner, C., Leeb, R., M\u00fcller-Putz, G.: BCI competition 2008-Graz data set A. IEEE Dataport (2024). https:\/\/doi.org\/10.21227\/katb-zv89","DOI":"10.21227\/katb-zv89"},{"key":"15_CR26","doi-asserted-by":"publisher","unstructured":"Cho, H., Ahn, M., Ahn, S., Kwon, M., Jun, S.C.: EEG datasets for motor imagery brain-computer interface. GigaScience 6(7), gix034 (2017). https:\/\/doi.org\/10.1093\/gigascience\/gix034","DOI":"10.1093\/gigascience\/gix034"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. J. Neural Eng. 14(6) (2017)","DOI":"10.1109\/SPMB.2017.8257015"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Lawhern, L.A, Solon, S.J., Waytowich, G.R., Gordon, A.R., Hung, M.G., Lance, J.A.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15 (5) (2018)","DOI":"10.1088\/1741-2552\/aace8c"},{"issue":"12","key":"15_CR29","doi-asserted-by":"publisher","first-page":"2773","DOI":"10.1109\/TNSRE.2020.3048106","volume":"28","author":"E Santamar\u00eda-V\u00e1zquez","year":"2020","unstructured":"Santamar\u00eda-V\u00e1zquez, E., Mart\u00ednez-Cagigal, V., Vaquerizo-Villar, F., Hornero, R.: EEG-Inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 28(12), 2773\u20132782 (2020). https:\/\/doi.org\/10.1109\/TNSRE.2020.3048106","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR 2024 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87657-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T05:04:02Z","timestamp":1746767042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87657-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031876561","9783031876578"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87657-8_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"10 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}