{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:18:47Z","timestamp":1785421127805,"version":"3.56.0"},"reference-count":80,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research, Development and Innovation Office","award":["HUN-REN RGH151414"],"award-info":[{"award-number":["HUN-REN RGH151414"]}]},{"award":["HUN-REN RGH151414"],"award-info":[{"award-number":["HUN-REN RGH151414"]}],"id":[{"id":"https:\/\/ror.org\/03g2am276","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge\u2013cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k = 5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.<\/jats:p>","DOI":"10.3390\/bios16070394","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:17:52Z","timestamp":1784647072000},"page":"394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Edge\u2013Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors"],"prefix":"10.3390","volume":"16","author":[{"given":"Sayantan","family":"Ghosh","sequence":"first","affiliation":[{"name":"Department of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Padmanabhan","family":"Sindhujaa","sequence":"additional","affiliation":[{"name":"Department of General Medicine, PSG Institute of Medical Sciences & Research, Coimbatore 641004, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4442-5073","authenticated-orcid":false,"given":"Pradakshana","family":"Senthil Kumar","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai 600123, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Mohan","sequence":"additional","affiliation":[{"name":"Department of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pachaiyappan","family":"Mahalakshmi","sequence":"additional","affiliation":[{"name":"Department of Instrumentation, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bal\u00e1zs","family":"Guly\u00e1s","sequence":"additional","affiliation":[{"name":"Cognitive Neuroimaging Centre, Experimental Medicine, Nanyang Technological University, Singapore 636921, Singapore"},{"name":"Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6727-1725","authenticated-orcid":false,"given":"Domokos","family":"M\u00e1th\u00e9","sequence":"additional","affiliation":[{"name":"Department of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parasuraman","family":"Padmanabhan","sequence":"additional","affiliation":[{"name":"Cognitive Neuroimaging Centre, Experimental Medicine, Nanyang Technological University, Singapore 636921, Singapore"},{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore"},{"name":"Division of Neuroradiology, Department of Medical Imaging, Faculty of Medicine, Semmelweis University, 1083 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1039\/C7LC00914C","article-title":"Wearable Sensors: Modalities, Challenges, and Prospects","volume":"18","author":"Heikenfeld","year":"2018","journal-title":"Lab Chip"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114756","DOI":"10.1016\/j.bios.2022.114756","article-title":"A Highly Stable Electrode with Low Electrode-Skin Impedance for Wearable Brain-Computer Interface","volume":"218","author":"Hsieh","year":"2022","journal-title":"Biosens. Bioelectron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"548","DOI":"10.3390\/biomed4040041","article-title":"Review of Multimodal Data Acquisition Approaches for Brain\u2013Computer Interfaces","volume":"4","author":"Ghosh","year":"2024","journal-title":"BioMed"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kalarikkal, N., Bhadrapriya, B.C., Anne Bose, B., Padmanabhan, P., Thomas, S., and Vadakke Matham, M. (2025). Biomedical Imaging: State of the Art. Multimodal Biomedical Imaging Techniques, Springer Nature.","DOI":"10.1007\/978-981-96-1124-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Bhuvanakantham, R., Sindhujaa, P., Harishita, P.B., Mohan, A., Guly\u00e1s, B., M\u00e1th\u00e9, D., and Padmanabhan, P. (2026). A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System. Biosensors, 16.","DOI":"10.20944\/preprints202601.1639.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1038\/nmat4671","article-title":"Pursuing prosthetic electronic skin","volume":"15","author":"Chortos","year":"2016","journal-title":"Nat. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/s44222-024-00194-1","article-title":"Skin-inspired soft bioelectronic materials, devices and systems","volume":"2","author":"Zhao","year":"2024","journal-title":"Nat. Rev. Bioeng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal Machine Learning: A Survey and Taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rashid, M., Sulaiman, N., Abdul Majeed, A.P.P., Musa, R.M., Nasir, A.F.A., Bari, B.S., and Khatun, S. (2020). Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Front. Neurorobot., 14.","DOI":"10.3389\/fnbot.2020.00025"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R., and Gorzelanczyk, E.J. (2021). Summary of over Fifty Years with Brain-Computer Interfaces\u2014A Review. Brain Sci., 11.","DOI":"10.3390\/brainsci11010043"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","article-title":"A Review on Machine Learning for EEG Signal Processing in Bioengineering","volume":"14","author":"Hosseini","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00530-010-0182-0","article-title":"Multimodal Fusion for Multimedia Analysis: A Survey","volume":"16","author":"Atrey","year":"2010","journal-title":"Multimed. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MC.2016.145","article-title":"The Promise of Edge Computing","volume":"49","author":"Shi","year":"2016","journal-title":"Computer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces: A 10 Year Update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/JBHI.2014.2328317","article-title":"Wearable, Wireless EEG Solutions in Daily Life Applications: What Are We Missing?","volume":"19","author":"Mihajlovic","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1900720","DOI":"10.1002\/admt.201900720","article-title":"Biosensors for Personal Mobile Health: A System Architecture Perspective","volume":"5","author":"Arumugam","year":"2020","journal-title":"Adv. Mater. Technol."},{"key":"ref_18","unstructured":"Banbury, C.R., Reddi, V.J., Lam, M., Fu, W., Fazel, A., Holleman, J., Huang, X., Hurtado, R., Kanter, D., and Lokhmotov, A. (2021). Benchmarking TinyML Systems: Challenges and Direction. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Saibene, A., Caglioni, M., Corchs, S., and Gasparini, F. (2023). EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. Sensors, 23.","DOI":"10.20944\/preprints202302.0096.v1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Sindhujaa, P., Kesavan, D.K., Guly\u00e1s, B., and M\u00e1th\u00e9, D. (2025). Brain-Computer Interfaces and AI Segmentation in Neurosurgery: A Systematic Review of Integrated Precision Approaches. Surgeries, 6.","DOI":"10.3390\/surgeries6030050"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA, 16\u201320 October 2018, Association for Computing Machinery.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_22","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. Affect. Comput."},{"key":"ref_23","unstructured":"Reiss, A. (2012). PAMAP2 Physical Activity Monitoring. UCI Mach. Learn. Repos."},{"key":"ref_24","unstructured":"Krilova, N., Kastalskiy, I., Kazantsev, V., Makarov, V.A., and Lobov, S. (2026, April 19). EMG Data for Gestures. Available online: https:\/\/archive.ics.uci.edu\/dataset\/481\/emg+data+for+gestures."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"BCI2000: A General-Purpose Brain-Computer Interface System","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1109\/TBME.2019.2921766","article-title":"Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power from Human Sensorimotor Cortex","volume":"67","author":"Nordin","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain Computer Interfaces, a Review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Panigrahi, N., and Mohanty, S.P. (2022). Fundamentals of EEG Signals, CRC Press.","DOI":"10.1201\/9781003241386-2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103101","DOI":"10.1016\/j.bspc.2021.103101","article-title":"Motor Imagery Classification in Brain-Machine Interface with Machine Learning Algorithms: Classical Approach to Multi-Layer Perceptron Model","volume":"71","author":"Sharma","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Golmohammadi, M., Harati Nejad Torbati, A.H., de Diego, S., Obeid, I., and Picone, J. (2019). Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00076"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ingolfsson, T.M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., and Benini, L. (2020). EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Toronto, ON, Canada, 11\u201314 October 2020, IEEE.","DOI":"10.1109\/SMC42975.2020.9283028"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TAFFC.2022.3169001","article-title":"TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition","volume":"14","author":"Ding","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TNSRE.2016.2641956","article-title":"A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State","volume":"25","author":"Myrden","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Affanni, A., Najafi, T.A., and Guerci, S. (2022). Development of an EEG Headband for Stress Measurement on Driving Simulators. Sensors, 22.","DOI":"10.3390\/s22051785"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/nrn.2016.164","article-title":"Closed-loop brain training: The science of neurofeedback","volume":"18","author":"Sitaram","year":"2016","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Almajidy, R.K., Mottaghi, S., Ajwad, A.A., Boudria, Y., Mankodiya, K., Besio, W., and Hofmann, U.G. (2023). A case for hybrid BCIs: Combining optical and electrical modalities improves accuracy. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1162712"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tangermann, M., M\u00fcller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., and M\u00fcller-Putz, G.R. (2012). Review of the BCI Competition IV. Front. Neurosci., 6.","DOI":"10.3389\/fnins.2012.00055"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/JIOT.2017.2750180","article-title":"Mobile Edge Computing: A Survey","volume":"5","author":"Abbas","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.future.2017.02.014","article-title":"Exploiting Smart e-Health Gateways at the Edge of Healthcare Internet-of-Things: A Fog Computing Approach","volume":"78","author":"Rahmani","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_41","unstructured":"Warden, P., and Situnayake, D. (2019). TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, O\u2019Reilly Media."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Schizas, N., Karras, A., Karras, C., and Sioutas, S. (2022). TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review. Future Internet, 14.","DOI":"10.3390\/fi14120363"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","article-title":"The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms","volume":"15","author":"Welch","year":"1967","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lobov, S., Krilova, N., Kastalskiy, I., Kazantsev, V., and Makarov, V.A. (2018). Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors, 18.","DOI":"10.20944\/preprints201804.0044.v1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_46","unstructured":"Schalk, G., Sarnacki, W.A., Joshi, A., McFarland, D.J., and Wolpaw, J.R. (2026, April 30). EEG Motor Movement\/Imagery Dataset v1.0.0. PhysioNet. Available online: https:\/\/www.physionet.org\/content\/eegmmidb\/1.0.0\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature Reduction and Selection for EMG Signal Classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.future.2017.04.036","article-title":"Towards Fog-Driven IoT eHealth: Promises and Challenges of IoT in Medicine and Healthcare","volume":"78","author":"Farahani","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/COMST.2022.3232576","article-title":"A Critical Survey of EEG-Based BCI Systems for Applications in Industrial Internet of Things","volume":"25","author":"Ajmeria","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.procs.2024.05.108","article-title":"Cloud Computing for Big Data Analytics: How Cloud Computing Can Handle Processing Large Amounts of Data and Improve Real-Time Data Analytics","volume":"237","author":"Galego","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hosseini, M.P., Soltanian-Zadeh, H., Elisevich, K., and Pompili, D. (2016). Cloud-Based Deep Learning of Big EEG Data for Epileptic Seizure Prediction. Proceedings of the IEEE Global Conference on Signal and Information Processing, Washington, DC, USA, 7\u20139 December 2016, IEEE.","DOI":"10.1109\/GlobalSIP.2016.7906022"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1016\/j.ijmedinf.2023.105026","article-title":"Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review","volume":"173","author":"Vos","year":"2023","journal-title":"Int. J. Med. Inform."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Alajlan, N.N., and Ibrahim, D.M. (2022). TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications. Micromachines, 13.","DOI":"10.3390\/mi13060851"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"133982","DOI":"10.1109\/ACCESS.2020.3010715","article-title":"Deep Neural Networks for Human Activity Recognition with Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection","volume":"8","author":"Gholamiangonabadi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Gamper, H., Tashev, I., Dong, S., Ma, S., Remaley, J., Holbery, J.D., and Yoon, S.H. (2020, January 25\u201330). Stress monitoring using multimodal bio-sensing headset. Proceedings of the Conference on Human Factors in Computing Systems\u2014Proceedings, Honolulu, HI, USA.","DOI":"10.1145\/3334480.3382891"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neunet.2022.03.025","article-title":"Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces","volume":"151","author":"Sun","year":"2022","journal-title":"Neural Netw."},{"key":"ref_59","first-page":"39","article-title":"Accelerating the Machine Learning Lifecycle with MLflow","volume":"41","author":"Zaharia","year":"2018","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_61","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/TBME.2010.2082539","article-title":"Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms","volume":"58","author":"Lu","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bobade, P., and Vani, M. (2020). Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data. Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA, Coimbatore, India, 15\u201317 July 2020, IEEE.","DOI":"10.1109\/ICIRCA48905.2020.9183244"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.bspc.2022.103496","article-title":"An Efficient Multi-Scale CNN Model with Intrinsic Feature Integration for Motor Imagery EEG Subject Classification in Brain-Machine Interfaces","volume":"74","author":"Roy","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNSRE.2024.3360194","article-title":"Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification","volume":"32","author":"Sartipi","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4494","DOI":"10.1109\/JSEN.2019.2899645","article-title":"Convolutional Neural Network Based Approach Towards Motor Imagery Tasks EEG Signals Classification","volume":"19","author":"Chaudhary","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Dai, M., Zheng, D., Na, R., Wang, S., and Zhang, S. (2019). EEG Classification of Motor Imagery Using a Novel Deep Learning Framework. Sensors, 19.","DOI":"10.3390\/s19030551"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Siirtola, P., and R\u00f6ning, J. (2020). Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection. Sensors, 20.","DOI":"10.3390\/s20164402"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"108632","DOI":"10.1016\/j.knosys.2022.108632","article-title":"From concept drift to model degradation: An overview on performance-aware drift detectors","volume":"245","author":"Bayram","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1109\/TBME.2020.3045720","article-title":"Domain Generalization in Biosignal Classification","volume":"68","author":"Dissanayake","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"78240","DOI":"10.52202\/075280-3420","article-title":"BIOT: Biosignal Transformer for Cross-data Learning in the Wild","volume":"36","author":"Yang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1515\/bmt-2014-0126","article-title":"An EEG\/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand","volume":"60","author":"Soekadar","year":"2015","journal-title":"Biomed. Tech."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, B., Zhang, C., Xiao, Y., and Wang, M.Y. (2019). An EEG\/EMG\/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Front. Neurorobot., 13.","DOI":"10.3389\/fnbot.2019.00007"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1109\/TCBB.2022.3175456","article-title":"Heterogeneous Multi-Task Learning with Expert Diversity","volume":"19","author":"Aoki","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s13534-018-00093-6","article-title":"Wearable EEG and beyond","volume":"9","author":"Casson","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1038\/s41467-025-57016-0","article-title":"Bio-inspired electronics: Soft, biohybrid, and \u201cliving\u201d neural interfaces","volume":"16","author":"Boufidis","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Leserri, D., Grimmelsmann, N., Mechtenberg, M., Meyer, H.G., and Schneider, A. (2022). Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network. Mathematics, 10.","DOI":"10.3390\/math10060932"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"114825","DOI":"10.1016\/j.bios.2022.114825","article-title":"Wearable artificial intelligence biosensor networks","volume":"219","author":"Zhang","year":"2023","journal-title":"Biosens. Bioelectron."}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/7\/394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T04:36:14Z","timestamp":1784781374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/7\/394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":80,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["bios16070394"],"URL":"https:\/\/doi.org\/10.3390\/bios16070394","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]}}}