Objective brain-based assessment of olfactory response to different odors holds the potential to minimize inconsistencies between screening consumer tests and actual, in-use tests, or market performance of odor products. Particularly, the use of electroencephalogram (EEG)-based technologies in developing such objective assessment solutions is attractive due to the fact that it provides a fast, non-invasive strategy for probing olfactory perception. To capture the complex nature of brain function involved in olfaction we are developing approaches for integrating different levels of information contained in the brain response to odor stimuli: local information scrutinized at different frequency bands and global functional information by means of interdependencies in the activity of different brain regions (brain networks), which reflects how information is processed and transferred across the brain.
The main goals of this project are: a) feasibility assessment for the objective categorization of the olfactory response to odor stimuli of different levels of perceived pleasantness and reward value of samples; b) development of portable (out-of-the lab) EEG-based systems for real-time assessment of neural response to olfactory stimuli; and c) development of next generation consumer testing platforms that enable objective brain-based assessment and provide support for behavioral experimental designs. To achieve these goals, our machine learning framework is used to decode brain signals and extract relevant information from EEG data in real time. The developed software platforms are used in pre-market consumer research by our industry partners.
Can an upper-limb amputee reach out and touch a soft, warm hand and feel it? Can a human-like, or a humanoid, robotic hand safely grasp a hot kitchen appliance (or, in a human-like reflex, react) ? How are multiple sensory streams - touch, temperature, force - integrated for sensory perception? How and where does the sensory embodiment and integration occur? Our work focuses on building computational models that explain how multiple sensory inputs are integrated by humans to enable our dream of giving a range of sensory perceptions and enabling control of prosthesis. This work is a collaboration between the Cognitive Engineering group at NUS and teams at Johns Hopkins University (JHU) and at the Technical University of Munich (TUM). We utilize our advanced sensor and robotics research and prosthesis technologies to conduct experiments and develop computer models of sensory processing in the brain. Our collaboration is focused on how a wide dynamic range of sensory stimuli, from innocuous to noxious, can be perceived and classified through the brain’s rhythms. We build computational models of the sensory networks to explain how multiple sensory streams are integrated in the brain and how multiple sensations, from innocuous (mild tactile) to noxious (painful), are accommodated.
The cognitive engineering program focuses on studying mental functions and how and where brain encodes information. We develop computational and analytical tools to analyze brain rhythms, mainly the EEG signal and functional MIR (fMRI) images. We develop network and graph models of the brain signals from different areas, study their interactions and connectivity. The goal is to use brain monitoring to assess mental workload, fatigue, alertness and arousal. This would have application in activities like driving and flying where both monotony or alertness can be a problem, and in human-machine interactions such as in autonomous driving. For elderly, we may be able to study onset of dementia, while in children we may be able study autism spectrum disorder.
Fatigue is a complex state, manifesting in general in the form drowsiness, deterioration in the vigilance level and reduced mental or physical performance, which may result in car crashes. Therefore, we developed a driving simulator system for driving fatigue tracking in order to prevent mental fatigue-related accidents. We developed two parallel analysis frameworks, which are power spectrum density based metrics for driving fatigue monitoring and functional connectivity based measures for fatigue assessment. We aim to develop an analysis framework for fatigue states monitoring in real time based on dry EEG systems. The estimated mental states can be used as bio-markers to automate the system when human operator is in risk-prone conditions. This design is potentially beneficial to other safety-critical areas including industrial control and air traffic management.
The HELIOS project focuses on developing a novel integrative framework for human machine interaction system (HMI) as a collaboration between human and modern machines, which will be applied to enhance motor, speech and neurocognition restoration of stroke patients. HELIOS will incorporate feedback to facilitate therapy course by establishing a trusted human-machine relationship through collaboration, conversation, and cognitive association. The overarching goal is to demonstrate the implementation of the devised framework in stroke rehabilitation therapy, targeting neurocognition and neuromotor restoration and enhancement through feedback and plasticity. Additionally,the research on machine intelligence will address both human and machine adaptation within real-life conditions. The research program has three main research pillars, which are Physical HMI (Assistive and augmentative motor control systems), Communication HMI (human-machine ‘speech chain’ for speech rehabilitation), and BM-I (HMI through Brain-Machine).
Creativity is defined as the ability to produce ideas that are both novel and useful. It is a crucial skill for effective problem-solving and decision-making, especially under extreme circumstances, in volatile, uncertain and increasingly complex environments. We designed an experimental paradigm where the participants were asked to perform Alternative Uses (AU, which participants were asked to think of alternative/unconventional ways to use everyday objects) task and Object Characteristics (OC, which participants were asked to list typical characteristics or properties of an object) task. We estimated relative power spectrum, global efficiency from brain networks constructed with the imaginary part of coherence and phase-to-amplitude coupling (PAC) as potential biomarker of creativity. Statistically significant differences between AU and OC were detected with PAC estimated within sensors in certain frequency pairs, which can be considered as the ground for both detecting and designing a connectomic biomarker of creativity.
Autonomous driving is becoming increasingly popular. What is the role of humans in autonomous driving? How do humans and machines (car) interact? We have developed a full virtual reality simulator for autonomous driving. We have developed another simulator for autonomous flying. The driver/student works in this simulator and his brain/mind is monitored during the autonomous experience. We study brain waves, eye movements, and give haptic/touch feedback. This project may be of interest to students interested in AI, augmented reality, autonomous driving. Good programming, AI/machine learning, and other skills or interests are desirable. This project has very practical real-life applications in human-machine interaction, and in advanced topics of AI and brain information processing.
Graduate Student Researcher
Center for Research and Technology Hellas (CERTH)
University of Cardiff
Nottingham Trent University
Sapienza University of Rome
AI Lead, Bifrost
Research Assistant (now @University of Pittsburgh)
MSc Student (now @Cambridge)
Research Assistant (now @Nanyang Technological University Singapore)
Seet S.M, Bezerianos A., Panou M., Bekiaris E., Thakor N.V, Dragomir A. 2022. Individual Susceptibility to Vigilance Decrement in Prolonged Assisted Driving Revealed by Alert-State Wearable EEG Assessment . IEEE Transactions on Cognitive and Developmental Systems, In Press.
Kepler F.V., Seet S.M, Hamano J., Saba M., Thakor N.V, Dragomir A. 2022. Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network. Brain Sciences, 12 (10), 1408.
Ding, K., Chen Y., Bose, R., Osborn, L.E., Dragomir, A and Thakor, N.V., 2022. Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs . Scientific Reports, 12, 20467 .
Bose R., Abbasi NI., Thakor N., Bezerianos A. and Dragomir A. 2021 Cognitive State Assessment and Monitoring - A Brain Connectivity Perspective. Handbook of Neuroengineering, Springer.
Dragomir A. and Omurtag A. 2021 Brain's Networks and their Significance in Cognition, Handbook of Neuroengineering, Springer.
Osborn, L.E., Ding, K., Hays, M.A., Bose, R., Iskarous, M.M., Dragomir, A., Tayeb, Z., Lévay, G.M., Hunt, C.L., Cheng, G. and Armiger, R.S., 2020. Sensory stimulation enhances phantom limb perception and movement decoding. Journal of neural engineering, 17(5), p.056006.
Seet, M., Harvy, J., Bose, R., Dragomir, A., Bezerianos, A. and Thakor, N., 2020. Differential impact of autonomous vehicle malfunctions on human trust. IEEE Transactions on Intelligent Transportation Systems.
Ding, K., Dragomir, A., Bose, R., Osborn, L.E., Seet, M.S., Bezerianos, A. and Thakor, N.V., 2020. Towards machine to brain interfaces: Sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees. Journal of neural engineering, 17(3), p.035002.
Tayeb, Z., Bose, R., Dragomir, A., Osborn, L.E., Thakor, N.V. and Cheng, G., 2020. Decoding of pain perception using EEG Signals for a Real-Time Reflex System in prostheses: A case Study. Scientific reports, 10(1), pp.1-11.
Bose, R., Wang, H., Dragomir, A., Thakor, N.V., Bezerianos, A. and Li, J., 2019. Regression-based continuous driving fatigue estimation: Toward practical implementation. IEEE Transactions on Cognitive and Developmental Systems, 12(2), pp.323-331.
Dragomir, A., Vrahatis, A.G. and Bezerianos, A., 2018. A network-based perspective in Alzheimer's disease: Current state and an integrative framework. IEEE journal of biomedical and health informatics, 23(1), pp.14-25.
Qi P., Ru H., Gao L., Zhang X., Zhou T., Tian Y., Thakor N., Bezerianos A., Li J., Sun Y., “Neural Mechanisms of mental fatigue revisited: New insights from the brain connectome,” Engineering, Vol. 5, pp. 276-286, 2019.
Wang, H., Dragomir, A., Abbasi, N.I., Li, J., Thakor, N.V. and Bezerianos, A., 2018. A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive neurodynamics, 12(4), pp.365-376.
Osborn, L.E., Dragomir, A., Betthauser, J.L., Hunt, C.L., Nguyen, H.H., Kaliki, R.R. and Thakor, N.V., 2018. Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Science robotics, 3(19).
Taya, F., Dimitriadis, S.I., Dragomir, A., Lim, J., Sun, Y., Wong, K.F., Thakor, N.V. and Bezerianos, A., 2018. Fronto‐Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue. Human brain mapping, 39(9), pp.3528-3545.
Sun Y., Lim J., Meng J., Kenneth K., Ph.D., Thakor N., Bezerianos A., “Discriminative analysis of brain functional connectivity patterns for mental fatigue classification,” Annals Biomed. Eng., Vol. 42, Issue 10, pp 2084-2094, 2014.
Borghini G., Arico P., Graziani I., Salinari S., Sun Y., Taya F., Bezerianos A., Thakor N.V., and Babiloni F., “Quantitative assessment of the training improvement in a motor-cognitive task by using eeg, ecg and eog signals,” Brain Topography, pp. 1–13, 2015.
Sun Y., Lee, R., Chen Y., Collinson S., N. Thakor, A. Bezerianos, Sim K., “Progressive gender differences of structural brain networks in healthy adults: a longitudinal diffusion tensor imaging study,” PLoS One, 10(3), e0118857, 2015, doi: 10.1371/journal.pone.0018857.
Taya, F., Sun, Y., F. Babiloni, N. Thakor, A. Bezerianos, “Brain enhancement through cognitive training: a new insight from brain connectome,” Frontiers Systems Neurosci., 9: e44, 2015.
Xu N., Sun T., Tsang W. M., Delgado-Martinez I., Lee S-H., Seshadri S., Xiang Z., Merugu S., Gu Y., Yen S-C., Thakor N. V. “Polymeric C-shaped cuff electrode for recording of peripheral nerve signal,” Sensors and Actuators B: Chemical, 210, 640-648, 2015.
Dimitriadis S., Sun Y., Laskaris, N., Thakor, N., Bezerianos, A., “Cognitive workload assessment based on the tensorial treatment of EEG estimates of cross-frequency phase interactions,” Annals Biomed. Eng., 43(4), 977-989, 2015.
Lee WW, Kukreja SL, Thakor NV., “CONE: Convex-optimized-synaptic efficacies for temporally precise spike mapping,” IEEE Trans Neural Netw Learn Syst., 2016 Mar 24. [Epub ahead of print] PMID: 27046881.
Li J., Lim J., Yu Chen, Wong K., Thakor N., Bezerianos A., and Sun Y., “Mid-task break Improves global integration of functional connectivity in lower alpha band,” Front. Hum. Neurosci., Jun 17;10:304. Doi: 10.3389/fnhum.2016.00304. eCollection 2016., PMCID: 27378894.
Cetinkaya-Fisgin A., Joo M. G., Ping X., Thakor N. V., Ozturk C.,Hoke A., Yang I. H., “Identification of fluocinolone acetonide to prevent paclitaxel induced peripheral neuropathy,” J Peripheral Nervous System, DOI: 10.1111/jns.12172, 2016. PMID: PMID: 27117347.
Taya F., de Souza J., Thakor N. V., Bezerianos A., “Comparison method for community detection on brain networks from neuroimaging data, Appl Netw Sci 1: 8, 2016. DOI: 10.1007/s41109-016-0007-y.
Dimitrakopoulos G. N., Sun Y., Ardian K., Thakor N. V., and Bezerianos A., “A method for cross-task mental workload classification based on brain connectivity,” Frontiers Human Neurosci., 2016. DOI=10.3389/conf.fnhum.2016.220.00002Borghini
G., Aricò P., Graziani I., Salinari S., Sun Y., Taya F, Bezerianos A.. Thakor N. V., Babiloni F., “Quantitative assessment of the training improvement in a motor-cognitive task by using EEG, ECG and EOG signals,” Brain Topography, Vol. 29 (Issue 1), pp 149-161, 2016.
Dimitriadis S., Sun Y., Laskaris N., Thakor N. and Bezerianos A., “ Revealing cross-frequency causal interactions during a mental arithmetic task through symbolic transfer entropy: a novel vector-quantization approach, IEEE Trans. Neural Systems Rehab Eng, vol. 24(1), pp. 1017-1028, 2016. 10.1109/TNSRE.2016.2516107
Taya F, Sun Y, Babiloni F, Thakor N, Bezerianos A., “Topological changes in the brain network induced by the training on a piloting task: An EEG-based functional connectome approach,” IEEE Trans Neural Syst Rehabil Eng., Vol. 26(2), pp. 263 – 271, 2016 Jun 16. [Epub ahead of print], PMID: 27333606 10.1109/TNSRE.2016.2581809.
Ren S., Li J., Taya F., deSouza J., Thakor N., Bezerianos A., “Dynamic functional segregation and Integration in human brain network during complex tasks,” IEEE Trans Neural Syst Rehabil Eng., Vol 25 (6) pp. 547-556, 2016, DOI: 10.1109/TNSRE.2016.2597961.
Sun Y., Lim J., Dai Z., Wong K.F., Taya F., Chen Y., Li J., Thakor N. V., Bezerianos A., “The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study,” NeuroImage, Vol. 152, 19-30, 2017.
Dai Z., de SouzaJ., Lim J., Ho P., Chen Y., Li J., Thakor N., Bezerianos A., Sun Y., “EEG cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands,” Frontiers Human Neurosci., Front Hum. Neurosci., 12 May 2017 | https://doi.org/10.3389/fnhum.2017.00237
Sciaraffa N., Borghini G., Aricò P.., Di Flumeri G., Colosimo A., Bezerianos A., Thakor N. V., and Babiloni F., “Brains interaction during cooperation: evaluating local properties of multiple-brain network,” Brain Sci., 7 (7), 90, 2017.
Li J-H, Thakor N. V., Bezerianos A., “Hemispherical Asymmetry of spectral power density in Beta band: A Study with and without Eexoskeleton,” Sci. Reports. Sci Rep. 2018; 8: 13470.. doi: 10.1038/s41598-018-31828-1, PMCID: PMC6128944, PMID: 30194397
Harvy J., Thakor N., Bezerianos A., Li J. “Between-frequency topographical and dynamic high-order functional connectivity for driving drowsiness assessment, “ IEEE Trans. Neural Systems Rehab Eng., 2019 (accepted).
Fumihiko T., Dimitriadis S.; Dragomir A., Lim J.; Sun, Y., Wong, Foong K., Thakor N. V., A. Bezerianos, “Fronto-parietal subnetworks flexibility compensates for cognitive decline due to mental fatigue,” Human Brain Mapping