Self-Supervised Learning for Near-Wild Cognitive Workload Estimation

Jiang, L., Eickhoff, S.B., Genon, S., Wang, G., Yi, C., He, R., Huang, X., Yao, D., Dong, D., Li, F., Xu, P.: Multimodal covariance network reflects individual cognitive flexibility. International Journal of Neural Systems 34(4), 2450018–17 (2024)

Article  PubMed  Google Scholar 

Dawson, N.V.: Physician judgment in clinical settings: methodological influences and cognitive performance. Clinical Chemistry 39(7), 1468–1480 (1993)

Article  CAS  PubMed  Google Scholar 

Horvat, M., Tement, S.: Self-reported cognitive difficulties and cognitive functioning in relation to emotional exhaustion: Evidence from two studies. Stress and Health 36(3), 350–364 (2020)

Article  PubMed  Google Scholar 

McMorris, T.: Cognitive fatigue effects on physical performance: The role of interoception. Sports Medicine 50(10), 1703–1708 (2020)

Article  PubMed  Google Scholar 

Xiao, L.X., Zeng, J., Chen, C., Chi, H.-L., Shen, G.Q.: Smart work package learning for decentralized facial fatigue monitoring. Computer-Aided Civil and Infrastructure Engineering 38(6), 799–817 (2023)

Article  Google Scholar 

Mohsenvand, M.N., Izadi, M.R., Maes, P.: Contrastive representation learning for electroencephalogram classification. In: Machine Learning for Health, pp. 238–253 (2020). PMLR

Belletier, C., Charkhabi, M., Andrade Silva, G., Ametepe, K., Lutz, M., Izaute, M.: Wearable cognitive assistants in a factory setting: a critical review of a promising way of enhancing cognitive performance and well-being. Cognition, Technology & Work 23(1), 103–116 (2021)

Article  Google Scholar 

Sankari, Z., Adeli, H.: Heartsaver: A mobile cardiac monitoring system for autodetection of atrial fibrillation, myocardial infarction, and atrio-ventricular block. Computers in Biology and Medicine 41(4), 211–220 (2011)

Article  PubMed  Google Scholar 

Graña, M., Aguilar-Moreno, M., De Lope Asiain, J., Araquistain, I.B., Garmendia, X.: Improved activity recognition combining inertial motion sensors and electroencephalogram signals. International Journal of Neural Systems 30(10), 2050053 (2020)

Teran-Pineda, D., Thurnhofer-Hemsi, K., Dominguez, E.: Human gait activity recognition using multimodal sensors. International Journal of Neural Systems 33(11), 2350058–15 (2023)

Article  PubMed  Google Scholar 

Hoilett, O.S., Twibell, A.M., Srivastava, R., Linnes, J.C.: Kick ll: A smartwatch for monitoring respiration and heart rate using photoplethysmography. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3821–3824 (2018). IEEE

Qaisar, S.M.: A computationally efficient eeg signals segmentation and de-noising based on an adaptive rate acquisition and processing. In: 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), pp. 182–186 (2018). IEEE

Ranjan, R., Sahana, B.C., Bhandari, A.K.: Ocular artifact elimination from electroencephalography signals: A systematic review. Biocybernetics and Biomedical Engineering 41(3), 960–996 (2021)

Article  Google Scholar 

Varone, G., Hussain, Z., Sheikh, Z., Howard, A., Boulila, W., Mahmud, M., Howard, N., Morabito, F.C., Hussain, A.: Real-time artifacts reduction during tms-eeg co-registration: a comprehensive review on technologies and procedures. Sensors 21(2), 637 (2021)

Article  PubMed  PubMed Central  Google Scholar 

Karakullukcu, N., Yilmaz, B.: Detection of movement intention in eeg-based brain-computer interfaces using fourier-based synchrosqueezing transform. International Journal of Neural Systems 32(1), 2150059 (2022). 15 pages

Adeli, H., Hung, S.-L.: An adaptive conjugate gradient learning algorithm for efficient training of neural networks. Applied Mathematics and Computation 62(1), 81–102 (1994)

Article  Google Scholar 

Hung, S.-L., Adeli, H.: A parallel genetic/neural network learning algorithm for mimd shared memory machines. IEEE Transactions on Neural Networks 5(6), 900–909 (1994)

Article  CAS  PubMed  Google Scholar 

Hassanpour, A., Moradikia, M., Adeli, H., Khayami, S.R., Shamsinejadbabaki, P.: A novel end-to-end deep learning scheme for classifying multi-class motor imagery electroencephalography signals. Expert Systems 36(6), 12494 (2019)

Article  Google Scholar 

Burns, A., Adeli, H., Buford, J.A.: Brain–computer interface after nervous system injury. The Neuroscientist textbf20(6), 639–651 (2014)

Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Computers in Biology and Medicine 100, 270–278 (2018)

Article  PubMed  Google Scholar 

Jiang, X., Bian, G.-B., Tian, Z.: Removal of artifacts from eeg signals: a review. Sensors 19(5), 987 (2019)

Article  PubMed  PubMed Central  Google Scholar 

Fridman, L., Reimer, B., Mehler, B., Freeman, W.T.: Cognitive load estimation in the wild. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–9 (2018)

Burns, A., Adeli, H.: Wearable technology for patients with brain and spinal cord injuries. Reviews in the Neurosciences 28(8), 913–920 (2017)

Article  PubMed  Google Scholar 

Albuquerque, I., Tiwari, A., Parent, M., Cassani, R., Gagnon, J.-F., Lafond, D., Tremblay, S., Falk, T.H.: Wauc: a multi-modal database for mental workload assessment under physical activity. Frontiers in Neuroscience 14, 549524 (2020)

Article  PubMed  PubMed Central  Google Scholar 

Xu, F., Yan, Y., Zhu, J., Chen, X., Gao, L., Liu, Y., Shi, W., Lou, Y., Wang, W., Leng, J., Zhang, Y.: Self-supervised eeg representation learning with contrastive predictive coding for post-stroke. International Journal of Neural Systems 33(12), 2350066–16 (2023)

Article  PubMed  Google Scholar 

Martinez-Murcia, F.J., Arco, J.E., Jiménez-Mesa, C., Segovia, F., Illán, I.A., Ramírez, J., Görriz, J.M.: Bridging imaging and clinical scores in parkinson’s disease progression via multimodal self-supervised deep learning. International Journal of Neural Systems 34(8), 2450043–16 (2024)

Article  PubMed  Google Scholar 

Shen, J., Yan, W., Qin, S., Zheng, X.: A self-supervised monocular depth estimation model with scale recovery and transfer learning for construction scene analysis. Computer-Aided Civil and Infrastructure Engineering 38(9), 1142–1161 (2023)

Article  Google Scholar 

Huang, J., Yang, X., Zhou, F., Li, X., Zhou, B., Lu, S., Ivashov, S., Giannakis, I., Kong, F., Slob, E.: A deep learning framework based on improved self-supervised learning for ground penetrating radar tunnel lining inspection. Computer-Aided Civil and Infrastructure Engineering 39(6), 814–833 (2024)

Article  Google Scholar 

George, S.H., Rafiei, M.H., Gauthier, L., Borstad, A., Buford, J.A., Adeli, H.: Computer-aided prediction of extent of motor recovery following constraintinduced movement therapy in chronic stroke. Behavioural Brain Research 329, 191–199 (2017)

Article  PubMed  Google Scholar 

George, S.H., Rafiei, M.H., Borstad, A., Adeli, H., Gauthier, L.V.: Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke. Behavioural Brain Research 333, 314–322 (2017)

Article  PubMed  PubMed Central  Google Scholar 

Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR

Liang, M., Chang, Z., He, S., Chen, Y., Gan, Y., Schlangen, E., Šavija, B.: Predicting early-age stress evolution in restrained concrete by thermo-chemomechanical model and active ensemble learning. Computer-Aided Civil and Infrastructure Engineering 37(14), 1809–1833 (2022)

Article  Google Scholar 

Castillo-Barnes, D., Martinez-Murcia, F.J., Jiménez-Mesa, C., Arco, J.E., SalasGonzalez, D., Ramírez, J., Görriz, J.M.: Non-linear weighting ensemble learning model to diagnose parkinson’s disease using multimodal data. International Journal of Neural Systems 33(8), 2350041 (2023). (20 pages)

Pak, H., Leach, S., Yoon, S.H., Paal, S.G.: A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups. Computer-Aided Civil and Infrastructure Engineering 38(11), 1520–1535 (2023)

Article  Google Scholar 

Shaffi, N., Subramanian, K., Vimbi, V., Hajamohideen, F., Abdesselam, A., Mahmud, M.: Performance evaluation of deep, shallow, and ensemble machine learning methods for the automated classification of alzheimer’s disease. International Journal of Neural Systems 34(7), 2450029–17 (2024)

Article  PubMed  Google Scholar 

Rafiei, M.H., Gauthier, L.V., Adeli, H., Takabi, D.: Self-supervised learning for electroencephalography. IEEE Transactions on Neural Networks and Learning Systems (2022)

Hart, S.G., Staveland, L.E.: Development of nasa-tlx (task load index): Results of empirical and theoretical research. Advances in Human Psychology: Human Mental Workload (1988)

Rafiei, M.H., Kelly, K.M., Borstad, A.L., Adeli, H., Gauthier, L.V.: Predicting improved daily use of the more affected arm poststroke following constraintinduced movement therapy. Physical Therapy 99(12), 1667–1678 (2019)

Article  PubMed  PubMed Central  Google Scholar 

Mohammadshirazi, A., Kalkhorani, V.A., Humes, J., Speno, B., Rike, J., Ramnath, R., Clark, J.D.: Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: a case study. Building and Environment 213, 108833 (2022)

Article  Google Scholar 

Abualigah, L.: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Computing and Applications 33(7), 2949–2972 (2021)

Article  Google Scholar 

Wang, J., Khishe, M., Kaveh, M., Mohammadi, H.: Binary chimp optimization algorithm (bchoa): A new binary meta-heuristic for solving optimization problems. Cognitive Computation 13(5), 1297–1316 (2021)

Article  Google Scholar 

Glover, F., Kochenberger, G., Xie, W., Luo, J.: Diversification methods for zeroone optimization. Journal of Heuristics 25(4), 643–671 (2019)

Article  Google Scholar 

Rafiei, M.H., Adeli, H.: A new neural dynamic classification algorithm. IEEE Transactions on Neural Networks and Learning Systems 28(12), 3074–3083 (2017)

Article  PubMed  Google Scholar 

Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1798–1828 (2013)

Article  PubMed  Google Scholar 

Ganjali, M., Mehridehnavi, A., Rakhshani, S., Khorasani, A.: Unsupervised neural manifold alignment for stable decoding of movement from cortical signals. International Journal of Neural Systems 34(1), 2450006–16 (2024)

Comments (0)

No login
gif