{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:03:27Z","timestamp":1769522607400,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization.<\/jats:p>","DOI":"10.3390\/info15060301","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T05:36:06Z","timestamp":1716528966000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Efficient EEG Signal Analysis for Emotion Recognition Using FPGA"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-9685","authenticated-orcid":false,"given":"M. R.","family":"Ezilarasan","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7753-0136","authenticated-orcid":false,"given":"Man-Fai","family":"Leung","sequence":"additional","affiliation":[{"name":"School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gonzalez, H.A., Yoo, J., and Elfadel, I.M. (2019, January 23\u201327). EEG-based emotion detection using un supervised transfer learning. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857248"},{"key":"ref_2","unstructured":"Sun, L., Liu, Y., and Beadle, P.J. (2005, January 28\u201330). Independent component analysis of EEG signals. Proceedings of the 2005 IEEE International Workshop on VLSI Design and Video Technology, Suzhou, China."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.compeleceng.2016.04.009","article-title":"A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns","volume":"53","author":"Mehmood","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zheng, W.-L., Peng, Y., Duan, R., and Lu, B.-L. (2014, January 6\u201311). EEG-based emotion recognition using discriminative graph regularized extreme learning machine. Proceedings of the 2014 International Joint Conference on Neural Network, Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889618"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s11517-015-1346-z","article-title":"Facial emotion recognition system for autistic children: A feasible study based on FPGA implementation","volume":"53","author":"Smitha","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Smitha, K.G., and Vinod, A.P. (2013, January 6\u20138). Low Complexity FPGA Implementation of Emotion Detection for Autistic Children. Proceedings of the 2013 7th International Symposium on Medical Information and Communication Technology (ISMICT), Tokyo, Japan.","DOI":"10.1109\/ISMICT.2013.6521709"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1016\/j.clinph.2011.04.026","article-title":"Automated artifact removal as preprocessing refines neonatal seizure detection","volume":"122","author":"Deburchgraeve","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7071","DOI":"10.1038\/s41598-021-86345-5","article-title":"Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification","volume":"11","author":"Gannouni","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3389\/fnsys.2020.00043","article-title":"EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder","volume":"14","author":"Liu","year":"2020","journal-title":"Front. Syst. Neurosci."},{"key":"ref_12","first-page":"1442","article-title":"Emotion recognition based on EEG feature maps through deep learning network","volume":"24","author":"Topic","year":"2021","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cogr.2021.04.001","article-title":"Review of the emotional feature extraction and classification using EEG signals","volume":"1","author":"Wang","year":"2021","journal-title":"Cogn. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3389\/fpsyt.2019.00368","article-title":"The Electrical Aftermath: Brain Signals of Posttraumatic Stress Disorder Filtered Through a Clinical Lens","volume":"10","author":"Butt","year":"2019","journal-title":"Front. Psychiatry"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TAFFC.2018.2854168","article-title":"Empirical Evidence Relating EEG Signal Duration to Emotion Classification Performance","volume":"12","author":"Pereira","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1019776","DOI":"10.3389\/fncom.2022.1019776","article-title":"EEG-based emotion recognition using hybrid CNN and LSTM classification","volume":"16","author":"Chakravarthi","year":"2022","journal-title":"Front. Comput. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ezilarasan, M.R., Pari, J.B., and Leung, M.-F. (2023). Reconfigurable Architecture for Noise Cancellation in Acoustic Environment Using Single Multiply Accumulate Adaline Filter. Electronics, 12.","DOI":"10.3390\/electronics12040810"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"117","DOI":"10.14445\/23488379\/IJEEE-V10I1P111","article-title":"An Efficient FPGA-Based Adaptive Filter for ICA Implementation in Adaptive Noise Cancellation","volume":"10","author":"Ezilarasan","year":"2023","journal-title":"SSRG Int. J. Electr. Electron. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1142\/S0218126623502948","article-title":"High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis","volume":"32","author":"Ezilarasan","year":"2023","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102019","DOI":"10.1016\/j.inffus.2023.102019","article-title":"Emotion recognition and artificial intelligence: A systematic review (2014\u20132023) and research recommendations","volume":"102","author":"Khare","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1049\/el.2020.2380","article-title":"Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network","volume":"56","author":"Khare","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.35940\/ijrte.B7808.0712223","article-title":"Classification of Emotion using Eeg Signals: An FPGA Based Implementation","volume":"12","author":"Darshan","year":"2023","journal-title":"Int. J. Recent Technol. Eng. (IJRTE)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"515104","DOI":"10.3389\/fnbot.2020.00025","article-title":"Current status, challenges, and possible solutions of EEG-based brain-computer interface: A comprehensive review","volume":"14","author":"Rashid","year":"2020","journal-title":"Front. Neurorobotics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"031002","DOI":"10.1088\/1741-2552\/abc902","article-title":"A survey on deep learning-based non-invasive brain signals: Recent advances and new frontiers","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15632","DOI":"10.1109\/TPAMI.2023.3299568","article-title":"Sparse Bayesian learning for end-to-end EEG decoding","volume":"45","author":"Wang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.62836\/iaet.vli1.002","article-title":"Automated pneumonia detection in chest x-ray images using deep learning model","volume":"1","author":"Li","year":"2022","journal-title":"Innov. Appl. Eng. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7303710","DOI":"10.1109\/JPHOT.2023.3302690","article-title":"Bubble-wave-mitigation algorithm and transformer-based neural network demodulator for water-air optical camera communications","volume":"15","author":"Li","year":"2023","journal-title":"IEEE Photonics J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1109\/TITS.2023.3264588","article-title":"A comprehensive implementation of road surface classification for vehicle driving assistance: Dataset, models, and deployment","volume":"24","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jafari, A., Page, A., Sagedy, C., Smith, E.A., and Mohsenin, T. (2015, January 22\u201324). A low power seizure detection processor based on direct use of compressively-sensed data and employing a deterministic random matrix. Proceedings of the 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), Atlanta, GA, USA.","DOI":"10.1109\/BioCAS.2015.7348376"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"67277","DOI":"10.1109\/ACCESS.2018.2870883","article-title":"Hardware Design of Real Time Epileptic Seizure Detection Based on STFT and SVM","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TBCAS.2017.2762721","article-title":"VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability","volume":"12","author":"Feng","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/TBCAS.2019.2947044","article-title":"Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection","volume":"13","author":"Elhosary","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Daoud, H., Abdelhameed, A.M., and Bayoumi, M. (2018, January 5\u20138). FPGA Implementation of High Accuracy Automatic Epileptic Seizure Detection System. Proceedings of the 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), Windsor, ON, Canada.","DOI":"10.1109\/MWSCAS.2018.8623883"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/6\/301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:47:53Z","timestamp":1760107673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/6\/301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["info15060301"],"URL":"https:\/\/doi.org\/10.3390\/info15060301","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}