Qasjet e inteligjencës artificiale dhe shkencës kompjuterike për klasifikimin e sinjalit eeg: Algoritmet dhe aplikimet
Abstrakti
Aplikimi i inteligjencës artificiale (AI) në shkencën mjekësore, veçanërisht përmes analizës së të dhënave të elektroencefalografisë (EEG), përfaqëson një hap të rëndësishëm drejt kuptimit dhe interpretimit të kompleksitetit të aktivitetit të trurit të njeriut. Përdorimi i të dhënave EEG përfshin fusha të ndryshme, duke përfshirë, por pa u kufizuar në, neurologjinë, psikiatrinë dhe ndërfaqet tru-kompjuter (BCI), duke ofruar një vështrim jo-invaziv në aktivitetet elektrike të trurit me implikime për diagnostikimin, ndërhyrjet terapeutike, dhe zhvillimin e teknologjive ndihmëse. Me avamcimin e teknikave të sofistikuara të machine-learning (ML) dhe deep-learning (DL), potenciali për të deshifruar dhe klasifikuar këto sinjale është rritur në mënyrë eksponenciale, duke premtuar përparime në shkencën mjekësore, neuropsikologji dhe më gjerë. Ky punim eksploron në mënyrë sistematike aplikimin e metodologjive të machine-learning të aplikuara në klasifikimin e sinjalit EEG, duke shqyrtuar si algoritmet tradicionale ML ashtu edhe modelet e avancuara DL. Studimi ynë përpilon dhe vlerëson algoritme të ndryshme, duke përfshirë Makinat Vektoriale Mbështetëse (SVM), Rrjetet Neurale Konvolucionale (CNN) dhe Rrjetat Neurale Recurrent (RNN), ndër të tjera, për efikasitetin e tyre në klasifikimin e saktë të sinjaleve EEG në një mori aplikimesh duke filluar nga konfiskimet, zbulimi deri në analizën e gjendjes mendore. Ne thellojmë nuancat teknike të këtyre algoritmeve, duke theksuar pikat e forta, kufizimet dhe grupet specifike të të dhënave EEG që ato janë aplikuar. Ky artikull rishikues shërben si një burim për studiuesit dhe praktikuesit në fushat e neuroshkencës, inteligjencës artificiale dhe më gjerë, duke ofruar njohuri mbi marrëdhënien e ndërlikuar midis aktivitetit të trurit dhe machine-learning.
Fjalët kyçe:
Artificial Intelligence, EEG, data acquisition, classification algorithms, emerging trends.Shkarkimet
References
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. Gupta, R., Alam, M. A., & Agarwal, P. (2020, August 1). Modified Support Vector Machine for Detecting Stress Level Using EEG Signals. Computational Intelligence and Neuroscience, 2020, 1–14. https://doi.org/10.1155/2020/8860841
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M. T. (2021, December 8). Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization. 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). https://doi.org/10.1109/
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. O’Shea, A., Lightbody, G., Boylan, G., & Temko, A. (2020, March). Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture. Neural Networks, 123, 12–25. https://doi.org/10.1016/j.neunet.2019.11.023
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. Zeng, W., Guo, Z., Shen, Y., Bashir, A. K., Yu, K., Al-Otaibi, Y. D., & Gao, X. (2021, January
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s21072339
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. Fatlawi, H. K., & Kiss, A. (2022, November 11). Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream. Entropy, 24(11),1641. https://doi.org/10.3390/e24111641
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. Bates, S., Hastie, T., & Tibshirani, R. (2023, May 15). Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association, 1–12. https://doi.org/10.1080/01621459.2023.2197686
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. Boonyakitanont, P., Lek-uthai, A., Chomtho, K., & Songsiri, J. (2020, March). A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomedical Signal Processing and Control, 57, 101702. https://doi.org/10.1016/j.bspc.2019.101702
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. Vivaldi, N., Caiola, M., Solarana, K., & Ye, M. (2021, November). Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification. IEEE Transactions on Biomedical Engineering, 68(11), 3205–3216. https://doi.org/10.1109/
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tbme.2021.3062502
References
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. Khosla, A., Khandnor, P., & Chand, T. (2020, April). A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybernetics and Biomedical Engineering, 40(2), 649–690. https://doi.org/10.1016/j.bbe.2020.02.002
. Rasheed, K., Qayyum, A., Qadir, J., Sivathamboo, S., Kwan, P., Kuhlmann, L., O’Brien, T., & Razi, A. (2021). Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. IEEE Reviews in Biomedical Engineering, 14, 139–155. https://doi.org/10.1109/rbme.2020.3008792
. Subasi, A., & Ismail Gursoy, M. (2010, December). EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems With Applications, 37(12), 8659–8666. https://doi.org/10.1016/j.eswa.2010.06.065
. Pooja, Pahuja, S., & Veer, K. (2021, May 4). Recent Approaches on Classification and Feature Extraction of EEG Signal: A Review. Robotica, 40(1), 77–101. https://doi.org/10.1017/s0263574721000382
. Kumar, P. N., & Kareemullah, H. (2014, February). EEG signal with feature extraction using SVM and ICA classifiers. International Conference on Information Communication and Embedded Systems (ICICES2014). https://doi.org/10.1109/icices.2014.7034090
. Gupta, R., Alam, M. A., & Agarwal, P. (2020, August 1). Modified Support Vector Machine for Detecting Stress Level Using EEG Signals. Computational Intelligence and Neuroscience, 2020, 1–14. https://doi.org/10.1155/2020/8860841
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. Rahman, A. A., Faisal, F., Nishat, M. M., Siraji, M. I., Khalid, L. I., Khan, M. R. H., & Reza,
M. T. (2021, December 8). Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization. 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). https://doi.org/10.1109/
biosmart54244.2021.9677770
. Masum, M., Shahriar, H., & Haddad, H. M. (2020, July). Epileptic Seizure Detection for Imbalanced Datasets Using an Integrated Machine Learning Approach. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://
doi.org/10.1109/embc44109.2020.9175632
. Jeon, J., & Cai, H. (2022, August). Multi-class classification of construction hazards via cognitive states assessment using wearable EEG. Advanced Engineering Informatics, 53, 101646. https://doi.org/10.1016/j.aei.2022.101646
. Sharma, N., Kolekar, M. H., & Jha, K. (2021, January). EEG based dementia diagnosis using multi-class support vector machine with motor speed cognitive test. Biomedical Signal Processing and Control, 63, 102102. https://doi.org/10.1016/j.bspc.2020.102102
. Thanigaivelu, P. S., Sridhar, S. S., & Sulthana, S. F. (2023, February 27). OISVM: Optimal Incremental Support Vector Machine-based EEG Classification for Brain-computer Interface Model. Cognitive Computation, 15(3), 888–903. https://doi.org/10.1007/s12559-023-10120-z
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iwcmc51323.2021.9498833
. Miah, M. O., Muhammod, R., Mamun, K. A. A., Farid, D. M., Kumar, S., Sharma, A., & Dehzangi, A. (2021, December). CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data. Journal of Neuroscience Methods, 364,
https://doi.org/10.1016/j.jneumeth.2021.109373
. Zhang, Y., Guo, H., Zhou, Y., Xu, C., & Liao, Y. (2023, January). Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion. Biomedical Signal Processing and Control, 79, 104237. https://doi.org/10.1016/j.bspc.2022.104237
. Zhou, Y., Wang, P., Gong, P., Wei, F., Wen, X., Wu, X., & Zhang, D. (2023). Cross-Subject Cognitive Workload Recognition Based on EEG and Deep Domain Adaptation. IEEE Transactions on Instrumentation and Measurement, 72, 1–12. https://doi.org/10.1109/tim.2023.3276515
. Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2022, November 21). EEG Based Emotion Recognition: A Tutorial and Review. ACM Computing Surveys, 55(4), 1–57. https://doi.org/10.1145/3524499
. Mattioli, F., Porcaro, C., & Baldassarre, G. (2021, December 1). A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface. Journal of Neural Engineering, 18(6), 066053. https://doi.org/10.1088/1741-2552/ac4430
. O’Shea, A., Lightbody, G., Boylan, G., & Temko, A. (2020, March). Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture. Neural Networks, 123, 12–25. https://doi.org/10.1016/j.neunet.2019.11.023
. Samavat, A., Khalili, E., Ayati, B., & Ayati, M. (2022). Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features. IEEE Access, 10, 24520–24527. https://doi.org/10.1109/access.2022.3155647
. Das Chakladar, D., Dey, S., Roy, P. P., & Dogra, D. P. (2020, July). EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomedical Signal Processing and Control, 60, 101989. https://doi.org/10.1016/j.bspc.2020.101989
. Yang, B., Zhu, X., Liu, Y., & Liu, H. (2021, July). A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model. Biomedical Signal Processing and Control, 68, 102581. https://doi.
org/10.1016/j.bspc.2021.102581
. Zeng, W., Guo, Z., Shen, Y., Bashir, A. K., Yu, K., Al-Otaibi, Y. D., & Gao, X. (2021, January
. Data-driven management for fuzzy sewage treatment processes using hybrid neural computing. Neural Computing and Applications, 35(33), 23781–23794. https://doi.org/10.1007/s00521-020-05655-3
. Cossu, A., Carta, A., Lomonaco, V., & Bacciu, D. (2021, November). Continual learning for recurrent neural networks: An empirical evaluation. Neural Networks, 143, 607–627. https://doi.org/10.1016/j.neunet.2021.07.021
. Du, Y., & Liu, J. (2022, June 1). IENet: a robust convolutional neural network for EEG based brain-computer interfaces. Journal of Neural Engineering, 19(3), 036031. https://doi. org/10.1088/1741-2552/ac7257
. Sikka, A., Jamalabadi, H., Krylova, M., Alizadeh, S., van der Meer, J. N., Danyeli, L., Deliano, M., Vicheva, P., Hahn, T., Koenig, T., Bathula, D. R., & Walter, M. (2020, February 24). Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks. Human Brain Mapping, 41(9), 2334–2346. https://doi.org/10.1002/hbm.24949
. Hashemnia, S., Grasse, L., Soni, S., & Tata, M. S. (2021, July 8). Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition. Frontiers in Systems Neuroscience, 15. https://doi.org/10.3389/fnsys.2021.617605
. Antoniou, E., Bozios, P., Christou, V., Tzimourta, K. D., Kalafatakis, K., G. Tsipouras, M., Giannakeas, N., & Tzallas, A. T. (2021, March 27). EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests. Sensors, 21(7), 2339. https://doi.org/10.3390/
s21072339
. Bastos, N. S., Marques, B. P., Adamatti, D. F., & Billa, C. Z. (2020, July 9). Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude. Computational Intelligence and Neuroscience, 2020, 1–11. https://doi.org/10.1155/2020/3598416
. Sha’abani, M. N. A. H., Fuad, N., Jamal, N., & Ismail, M. F. (2020). kNN and SVM Classification for EEG: A Review. Lecture Notes in Electrical Engineering, 555–565. https://doi.org/10.1007/978-981-15-2317-5_47
. Fatlawi, H. K., & Kiss, A. (2022, November 11). Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream. Entropy, 24(11),1641. https://doi.org/10.3390/e24111641
. Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021, January). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225,117465. https://doi.org/10.1016/j.neuroimage.2020.117465
. Chaddad, A., Wu, Y., Kateb, R., & Bouridane, A. (2023, July 16). Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. Sensors, 23(14), 6434. https://doi.org/10.3390/s23146434
. Bates, S., Hastie, T., & Tibshirani, R. (2023, May 15). Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association, 1–12. https://doi.org/10.1080/01621459.2023.2197686
. Alpaydin, E. (2020, March 24). Introduction to Machine Learning, fourth edition. MIT Press. http://books.google.ie/books?id=uZnSDwAAQBAJ&printsec=frontcover&dq=Introduction+-to+Machine+Learning,+fourth+edition&hl=&cd=1&source=gbs_api
. Boonyakitanont, P., Lek-uthai, A., Chomtho, K., & Songsiri, J. (2020, March). A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomedical Signal Processing and Control, 57, 101702. https://doi.org/10.1016/j.bspc.2019.101702
. Vivaldi, N., Caiola, M., Solarana, K., & Ye, M. (2021, November). Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification. IEEE Transactions on Biomedical Engineering, 68(11), 3205–3216. https://doi.org/10.1109/
tbme.2021.3062502