Jose C. PRINCIPE

University of Florida

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Cuntai GUAN

Nanyang Technological University

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Amy ORSBORN

University of Washington

Dr. Amy Orsborn is a Clare Boothe Luce Assistant Professor in Electrical & Computer Engineering and Bioengineering at the University of Washington. She works at the intersection of engineering and neuroscience to develop therapeutic neural interfaces to restore motor function. Her lab explores neural interfaces as adaptive closed-loop systems that engage plasticity in the brain. She designs engineering approaches that shape neural adaptation to improve system performance, and uses neural interfaces as a tool to study brain learning. The lab also specializes in system integration for advancing neurotechnologies to study large-scale neural circuits in non-human primates towards clinically-translatable technologies. Among her honors, she received a L’Oreal USA for Women in Science postdoctoral award, the L’Oreal USA Changing The Face of STEM award, a Google Faculty Research Award, and is an Interdisciplinary Rehabilitation Engineering research fellow. She completed her Ph.D. at the UC Berkeley/UCSF Joint Graduate Program in Bioengineering, and was a postdoctoral researcher at NYU’s Center for Neural Science.

Motor brain-machine interfaces (BMIs) hold great promise for restoring and rehabilitating motor function. BMIs directly map neural activity to the movements of an external device via a decoding algorithm. Both the brain and decoder algorithm can adapt in a BMI via neural plasticity and machine learning, respectively. Experimental work has demonstrated that synergistic learning between the brain and decoder (“co-adaptation”) may provide many potential benefits, like skillful user performance that can be maintained for long periods of time. While co-adaptation has been demonstrated, how to optimize the performance in this two-learner system. I will present recent work exploring neural dynamics in co-adaptive BMIs to highlight potentially sub-optimal user learning. I will then present a new conceptual and mathematical framework to model co-adaptation, reframing it as a dynamic game between the brain and decoder. Our model captures existing known properties of co-adaptation, while also enabling new approaches to actively shape user learning. We propose this framework could ultimately be used to overcome potentially sub-optimal user learning in existing systems to optimize performance. Co-adaptive BMIs that actively guide the user to a desired neural strategy may also have broad applications for rehabilitation.

Ning JIANG

West China Hospital, Sichuan University

Dr. Jiang’s research leverages signal processing methods and artificial intelligence algorithms for biological signals, such as Electroencephalogram (EEG) and Electromyography (EMG), for neurorehabilitation engineering applications. In particular, his research focuses on new human-machine interfacing (HMI) technologies, including brain-computer interfaces (BCI) and muscle-man-interfaces (MMI). His innovations have resulted in new and more efficient upper limb prosthetic control technologies and accelerated rehabilitation of motor functions for patients suffering from disorders, such as stroke. His research has also been applied more broadly, to HMI for applications in kinesiology, neural plasticity (cortical and peripheral), ergonomics and other related areas. These contributions have not only advanced the scientific knowledge of the field but have also achieved direct impacts on the biomedical industry.

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Yuxiao YANG

University of Central Florida

Yuxiao Yang is currently an Assistant Professor of Electrical and Computer Engineering (ECE) at the University of Central Florida (UCF) and will join Zhejiang University as an Assistant Professor starting Fall 2022. Prior to joining UCF, he was a postdoc at the University of Southern California (USC). He received the Ph.D. degree in ECE from USC in 2019. He received the B.S. degree from Tsinghua University in 2013. His research has centered on designing closed-loop brain-machine interface systems for neural decoding and control, aiming to provide new therapies for neurological and neuropsychiatric disorders. He has published in prestigious neural engineering journals, including cover articles in Nature Biotechnology and Nature Biomedical Engineering. He received the Annual Brain-Computer Interface Award in 2019, the IEEE EMBS Best Student Paper Award in 2015, and the McMullen Fellowship in 2013.

The precise control of brain states using brain stimulation such as deep brain stimulation (DBS) is critical in developing new therapies for treating brain disorders such as treatment-resistant depression. However, the complexity of brain network dynamics and the existence of disturbances and noise make the precise control of brain states challenging. In this talk, we will introduce our recent progress in developing model-based, robust, and adaptive neuromodulation algorithms for closed-loop control of brain states. We will cover the construction of data-driven models that can predict the responses of large-scale brain networks during direct electrical stimulation, the design of robust adaptive neuromodulation algorithms that can combat disturbances and noise, and their validation in non-human primate experiments and simulation experiments using neurophysiological models of various disorders.

Dario FARINA

Imperial College London

Professor Farina has been Full Professor at Aalborg University, Aalborg, Denmark, (until 2010) and at the University Medical Center Göttingen, Georg-August University, Germany, where he founded and directed the Institute of Neurorehabilitation Systems (2010-2016) until he moved to Imperial College London as Chair in Neurorehabilitation Engineering.
His research focuses on biomedical signal processing, neurorehabilitation technology, and neural control of movement. Within these areas, he has (co)-authored approximately 400 papers in peer-reviewed Journals and >500 conference abstract and papers. He has been the President of the International Society of Electrophysiology and Kinesiology (ISEK) (2012-2014) and is currently the Editor-in-Chief of the official Journal of this Society, the Journal of Electromyography and Kinesiology. He is also currently an Editor for IEEE Transactions on Biomedical Engineering and the Journal of Physiology, and previously covered editorial roles in several other Journals.

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Yiwen WANG

The Hong Kong University of Science and Technology

Yiwen Wang received the B.S. and M.S. degrees in electrical engineering from University of Science and Technology of China (USTC), Hefei, Anhui, China in 2001 and 2004 respectively. Under the guidance of Dr. Jose C. Principe (IEEE Fellow), she received the Ph.D. degree from University of Florida, Gainesville, FL, USA in 2008. She worked as a Fellow Intern in Siemens Corporation Research, Princeton, NJ, USA in 2007. In 2008, she joined the Department of Electronics and Computer Engineering as a Research Associate at the Hong Kong University of Science and Technology, Kowloon, Hong Kong, and corporated with Dr. Bertram Shi (IEEE Fellow). From 2010 to 2016, she worked as an Associate Professor at Zhejiang University, Hangzhou, China. In 2017, she joined the faculty of Department of Electronics and Compter Engineering and Division of Biomedical Engineering at the Hong Kong University of Science and Technology, Kowloon, Hong Kong. Her research interests are in neural decoding of brain-machine interfaces, adaptive signal processing, computational neuroscience, neuromorphic engineering. She serves in the IEEE EMBS Neural Engineering Tech Committee, and is an Associate Editor of the IEEE Transactions of Neural Systems and Rehabilitation Engineering. She holds one US patent and has authored more than 70 peer-reviewed publications.

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Hu TAO 

Shanghai Institute of Microsystem and Information Technology

Dr. Tiger H. Tao is currently the Vice Director of Shanghai Institute of Microsystem and Information Technology, CAS, the Vice Director of the State Key Lab of Transducer Technology, and the Founding Director of 2020 X-Lab. Dr. Tao received his Ph.D. in Mechanical Engineering with the Best Dissertation Award from Boston University, in 2010. His research interests have mainly focused on the cutting-edge research of biotechnology and information technology (BTIT), including the Brain-Computer Interfaces (BCIs), AI sensors and chips, biomaterials and medical implants. Dr. Tao is the Co-Founder and the Chief Scientist of NeuroXess, a start-up company focused on BCI technologies. Dr. Tao has won the Top Award of World Artificial Intelligence Conference (WAIC 2021), Shanghai Young Scientist Award for Outstanding Contribution (2020), and Young Scientist Award of CAS (2021).

Implanted neural interfaces have drawn great attentions recently. Instability of chronic recording is a main challenge for conventional neural interfaces. Novel neural probes with improved long-term performance have been developed based on advanced materials and engineered structures. In this talk, I will introduce these emergent innovations contributing to chronic stable recording from the perspectives of materials, structures, fabrication and implantation methods. These advances make possible further developments in neuroscience research related to neural decoding, neural circuit mechanism analysis, and neurological disease treatment.

Xiao LIU 

Fudan University

Dr Xiao Liu is currently a professor at the School of Information Science and Technology, Fudan University. Before that he worked at the University College London and Brunel University. He received his PhD from University College London. He is the recipient of the UK Research and Innovation Fellowship, Okawa Foundation Research Grant, and NSFC Research Fund for International Excellent Young Scientists. His research interests include analog and mixed-signal integrated circuits for implanted devices, microelectronic sensors and wearable technologies.

Damage or degradation to the central and peripheral nervous systems due to injury or disease results in loss of neural functions in various parts of the body. Neuroprostheses use implanted devices that interface with the nervous system to assist in partial restoration of function and mobility. The “brain” of an implanted device is its electronics which are usually implemented on a custom integrated circuit. The ASIC senses neural signals and provides electrical stimulus to the target neural tissue, possibly forming a closed loop control.

Dong SONG 

University of Southern California

Dr. Dong Song is Research Associate Professor of Biomedical Engineering, Director of the Neural Modeling and Interface Laboratory at the University of Southern California (USC).  Dr. Song received his B.S. degree in Biophysics from the University of Science and Technology of China in 1994, and his Ph.D. degree in Biomedical Engineering from the University of Southern California in 2004.  He became a Research Assistant Professor in 2006, and a Research Associate Professor in 2013, at the Department of Biomedical Engineering, USC.  The overarching goal of his research is to develop biomimetic devices that can be used to treat neurological disorders.  His group uses a combined experimental and computational strategy to (1) understand how brain regions such as the hippocampus perform cognitive functions, (2) develop next-generation modeling and neural interface methodologies to investigate brain functions during naturalistic behaviors, and (3) build cortical prostheses that can restore cognitive functions lost in diseases or injuries.  He received the James H. Zumberge Individual Award at USC in 2008, the Outstanding Paper Award of IEEE Transactions on Neural Systems and Rehabilitation Engineering in 2013, and the Society for Brain Mapping and Therapeutics Young Investigator Award in 2018.  Dr. Song has published over 180 peer-reviewed journal articles, book chapters, and reviewed conference papers.  He is a member of American Statistical Association, Biomedical Engineering Society, IEEE, Society for Neuroscience, Society for Brain Mapping and Therapeutics, and National Academy of Inventors.  Dr. Song’s research is supported by DARPA, NSF, and NIH.

We build a double-layer multiple temporal-resolution classification model for decoding memory categories from single-trial spatio-temporal patterns of spikes.  The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier.  In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations.  A wide range of temporal resolutions of neural features are included by using a large number of classifiers with different numbers of B-spline knots.  Each classifier serves as a base learner to classify spatio-temporal patterns into the probability of the output label with a single temporal resolution.  A bootstrap aggregating strategy is used to reduce estimation variances of these classifiers.  In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions.  This classifier serves as a meta learner that fuses multiple temporal resolutions to classify spatio-temporal patterns of spikes into binary output labels.  We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks.  Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size.  The double-layer multi-resolution classifier consistently outperforms the best single-layer single-resolution classifier by extracting and utilizing multi-resolution spatio-temporal features of spike patterns in the classification.

Juan Álvaro GALLEGO 

Imperial College London

Juan is a lecturer at the Department of Bioengineering, where he leads the Behavioural Neuroscience and Neuroprosthetics (Be.Neuro) Lab. His research focuses on understanding how the brain learns and controls movement. He also works to leverage this knowledge and develop neuroprosthetic technologies that restore movement to people with motor disability, especially Parkinson’s disease, tremor, and paralysis. In the lab, they pursue these goals based on a combination of behavioural experiments, high-yield neural recordings, data analysis techniques, and computational models.

Neuroprosthetics are affording paralysed users “mental control” of computer cursors or robots, or even of electrical stimulators that reanimate their own limbs. This control is achieved by mapping the activity of hundreds of motor cortical neurons recorded with implanted electrodes into appropriate control signals. Albeit impressive feats, these neuroprosthetics still face several challenges including generalisation across tasks, across users, and the neurons being recorded changing even during a single experiment. All these challenges make neuroprosthetic control far less proficient than how skilfully and effortlessly we can wield other tools.

In this talk, I will discuss how recent advances in neuroscience are allowing us to tackle these various challenges. The central hypothesis is that the brain works not by independently modulating the activity of single neurons, but based on specific population-wide activity patterns that define a “neural manifold.” I will then show how by leveraging these ideas, our group is addressing the challenges outlined above. These results provide evidence in favour of a “neural manifold” view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of neuroprosthetics.

Nitish V. THAKOR 

Johns Hopkins University

Nitish V. Thakor is a Professor of Biomedical Engineering, Electrical and Computer Engineering and at Johns Hopkins University since 1983. He also joined the National University of Singapore in 2012 and served as the Founding Director of Singapore Institute for Neurotechnology (SINAPSE). Prof. Thakor’s technical expertise is in the fields of Medical Instrumentation and Neuroengineering, where he has carried out research on many technologies for brain monitoring, implantable neurotechnologies, neuroprosthesis and brain-machine interface. He has published over 430 refereed journal papers (GH Index 86), obtained 16 US and international patents and co-founded 3 active companies. He was previously the Editor in Chief of IEEE Transactions on Neural Systems and Rehabilitation Engineering, and currently the EIC of Medical and Biological Engineering and Computing (Springer/Nature). He is the Editor of an upcoming authoritative reference Handbook of Neuroengineering. Prof. Thakor is a recipient of the Technical Achievement Award (Neuroengineering) as well as the Academic Career Award from the IEEE Engineering in Medicine and Biology Society. He received a Research Career Development Award from the National Institutes of Health and a Presidential Young Investigator Award from the National Science Foundation, and is a Fellow of the American Institute of Medical and Biological Engineering, Life Fellow of IEEE, Biomedical Engineering Society, and International Federation of Medical and Biological Engineering. He is a recipient of a Distinguished Alumnus Award from Indian Institute of Technology, Bombay, India, and a Centennial Medal from the University of Wisconsin School of Engineering. He was elected to the National Academy of Inventors in 2021.

Upper limb prosthesis with multiple fingers and dexterous manipulation have advanced significantly, with new research and commercial hands being available for amputees and researchers. However, their design and control strategies are more modeled after robotic mechanisms than anthropomorphic solutions. Therefore, this talk will focus on making progress towards human-like, human-friendly solutions that take inspiration from human hand and fingers and utilize strategies for control that mimics biomimetic approach and sensing that mimics bio/neuro inspired approaches. I will present the recent development of prosthetic hands, and then progress towards ‘soft’ prosthetic finger and hand design. I will next present the biomimetic decoding and controls strategies. Prosthetic hand/finger movements interact with the environment through sensing, and sensory feedback. I will present progress towards tactile and proprioceptive sensing and feedback in the design and configuration of prosthetic finger and hand design. The talk will summarize the benefits and challenges of biomimetic/neuro inspired solutions for prosthetic hand development and translation.

Rajesh P. N. RAO 

University of Washington

Rajesh Rao’s research spans the areas of computational neuroscience, brain-computer interfaces, and artificial intelligence. His current research focuses on how the brain learns models of the world from observations and actions, how the brain makes decisions based on noisy sensory information, and how brain signals and AI can be combined to build brain co-processors for restoring and augmenting neural function.
Rajesh received his Ph.D. from the University of Rochester and was a Sloan postdoctoral fellow at the Salk Institute for Biological Studies in San Diego before arriving at the UW. He is the recipient of a Guggenheim Fellowship, a Fulbright Scholar award, an NSF CAREER award, an ONR Young Investigator Award, a Sloan Faculty Fellowship, and a David and Lucile Packard Fellowship for Science and Engineering. He is the author of the textbook Brain-Computer Interfacing: An Introduction and has co-edited two books, Probabilistic Models of the Brain and Bayesian Brain. He directs the Neural Systems Laboratory at UW CSE. With Adrienne Fairhall, he taught the first MOOC on Computational Neuroscience. His not-so-copious spare time is devoted to Indian art history and to understanding the ancient undeciphered script of the Indus civilization, a topic on which he has given a TED talk.

Biological evolution has endowed the human brain with computational capabilities that have made us the most adaptive and inventive species on the planet. These capabilities are beginning to be replicated or in some cases surpassed by recent advances in the field of artificial intelligence (AI). In this talk, I will explore the tantalizing possibility of merging AI and the brain using the concept of brain co-processors. I will present examples of brain co-processors developed at the Center for Neurotechnology that can be used to rewire the brain to restore lost function. I will also present other brain co-processors that augment the human brain by enabling direct brain-to-brain communication and sensory augmentation. I will conclude by discussing the ethical and societal implications of brain co-processors and the need for appropriate guidelines and regulations as tech companies begin investing in such neurotechnologies.

Sung-Phil KIM 

Ulsan National Institute of Science and Technology

Sung-Phil is an associate professor at the Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST). His major research focuses on developing a brain-computer interface (BCI) in both non-human primates and humans. He is currently conducting research projects related to: bi-directional intracortical BCIs, EEG-based BCIs, EEG biomarkers for mental disorders, neuromarketing, neuron-inspired AI, cognitive decoding, tactile intelligence in robots and BCI-assisted neurorehabilitation. Dr. Kim earned his Ph.D and M.S. degrees from the Department of Electrical and Computer Engineering at the University of Florida in 2005 for the development of decoding models at the Computational NeuroEngineering Lab, and worked in the neural interface research team as a postdoctoral researcher at Brown University for the development of intracortical BCIs in humans with tetraplegia.

While recent advances in brain-computer interfaces (BCIs) have demonstrated the feasibility of decoding high-level brain functions such as spoken language, decision-making or handwritings, it is relatively unknown whether musical information can also be decoded from brain activity. In this study, we investigated the feasibility of decoding musical pitch information from human scalp EEG signals. Ten musically-trained and ten non-trained people participated in the experiment, where participants imagined the production of one of the seven pitches (C4, D4, E4, F4, G4, A4 and B4) randomly guided by a visual cue on the piano keyboard. By analyzing the spectral power of participants’ EEG signals in five frequency bands, including delta, theta, alpha, beta and gamma, we observed marked anti-symmetric patterns of spectral power between hemispheres where the spectral power increased in one side and decreased in the other side as the pitch height increased. More interestingly, these patterns were reversed between the low-frequency (delta, theta and alpha) and high-frequency (beta and gamma) bands. Using the multi-class SVM and K-fold cross-validation, we could decode spectral power features into seven classes with accuracy of 35.68±7.47% on average (maximum of 50.71%), which was way higher than a chance level (14.28%). There was no significant difference in decoding accuracy between the participant groups (p > 0.05). Our results may suggest a feasibility to decode musical pitch information from non-invasive EEG signals and further development of musical BCIs.

Yong HU 

The University of Hong Kong

Dr Hu has been working in the research area of neural engineering for more than 30 years. He has conducted more than 30 research projects with granted from Hong Kong RGC, ITF, S.K. Yee Medical Foundation, NSFC, National Key R&D Program and Shenzhen Innovation fund etc. He published 10 book chapters, and more than 300 articles, a total 3647 citation in Scopus with H-index of 30.
He is a senior member of IEEE and Chinese BME society; Council Board member of International Association of Neurorestoratology(IANR); Vice Chairmen of Chinese Committee of IANR; Vice president, Clinical Neuroelectrophysiological Committee of Chinese Research Hospital Association, Vice president of basic science sub-committee of Chinese Association of Spine and Spinal Cord; Vice president of Spinal electrophysiology sub-committee, Chinese Association of Spine and Spinal Cord; Vice president of Chinese Committee of Biomedical Sensor Technology, Chinese BME society, Council committee member in Chinese Sub-society of Medical Neural Engineering, Chinese BME society. Dr. Hu was awarded several prestigious awards for his academic contributions, including Second-Class Prize of 2019 China State Scientific and Technological Progress Award, Second Prize of Tianjin Scientific and Technological Progress Award in 2018, Silver award in International Exhibition of Inventions of Geneva in 2019, general awards of Brain-Controlled Robot Contest of World Robot Conference in 2019 and 2020, Universitas 21 Fellowship; Oldendorf Award from American Society of Neuroimaging; and Sino-UK Fellowship, Macnab/Larocca Research Fellowship from the International Society for the Study of the Lumbar Spine (ISSLS), etc.

Soft robot gloves controlled by brain computer interface (BCI) have been reported as an effective neurorehabilitation tool for post-stroke hand function therapy. Brain computer interface based on motor imagination (MI) has been widely used to improve hand function after stroke through robot assisted equipment. This study proposed an alternative BCI paradigm, i.e. steady-state visual evoked potential (SSVEP). The hand function recovery of SSVEP-BCI controlled soft robot glove rehabilitation is better than that of single robot glove rehabilitation, which is equivalent to results of previously reported MI-BCI robot hand rehabilitation. It proves the feasibility of SSVEP-BCI controlled soft robot gloves in post-stoke hand function rehabilitation.

Yu SUN 

Zhejiang University

Yu Sun is currently a research professor with the Key Laboratory for Biomedical of Engineering of Ministry of Education of China, Zhejiang University, and a provincial expert of Zhejiang province. His research interests include the area of neural engineering, brain-computer interface, machine learning, and multimodal neuroimaging data analytics with particular focus on human connectome. He is an associate editor of IEEE Transactions on Neural Systems And Rehabilitation Engineering (TNSRE), and Medical and Biological Engineering and Computing, and a corresponding expert of Engineering. He received EMBS Best Paper Award, Transactions on Biomedical Engineering, 2021, Early-career research award, Chinese Psychological Society, EEG-related Technical Committee, 2021, and the Best Poster Award in the 2013 IEEE Life Sciences Grand Challenges Conference.

Maintaining high efficiency in mentally demanding activities is a daily experience, while the reliable assessment of the cognitive load states can monitor the task-related burden in real-world scenarios therefore promoting the working efficiency. In the nascent field of neuroergonomics, quantitative mental workload assessment is one of the most important issues. Within the presentation, we would introduce the most recent advances in mental workload assessment through utilizing objective biomarkers. Most importantly, we would focus on 1) the implication of mental workload, with particular interests on the revealing of the underlying neural mechanisms, and 2) the application promotion in various close-to-real scenarios, with an ultimate aim of developing practical techniques for mental workload detection for real implementation.

neural interfacing hardware

101      SWCNTs/PEDOT:PSS modified microelectrode arrays for dual-mode detection of electrophysiological signals and dopamine concentration in striatum under isoflurane anesthesia

102      Automatic and quantitative electroencephalographic characterization of drug-resistant epilepsy in neonatal KCNQ2 epileptic encephalopathy

103      Fully autonomous brain-machine interface experiments in animal’s home-cage

104      Novel 3D nonhuman primate behavior analysis system

105      Multi-shank parylene penetrating probe for brain research

106      Active or passive mode? Interaction differences within and between the central and peripheral nervous systems

107      A high-precision programmable electrical stimulation device with wide stimulation voltage compliance range and redundant digital calibration

108      Intelligent closed-loop neuro stimulation system

109      Longitudinal neuromuscular changes of amputee patients during surface electromyograph prosthesis training

110      Implantable flexible wireless passive sacral neuromodulation stimulator based on PT symmetry

111       Evaluation of RF heating for SCS implants in MRI at 1.5T and 3T

neural signal processing and algorithm

201      Domain shift quantification and elimination for sEMG decoding in upper-limb kinematics estimation

202      Spiking graph convolutional networks

203      Structural insight into the individual variability architecture of the functional brain connectome

204      EEG-based biometric recognition with multi-source domain adaptation

205      Fusion of spatial, temporal and spectral EEG signatures improves multilevel cognitive load prediction

206      Decoding memory from neuronal spikes using ensemble learning

neural signal processing and algorithm

207      Estimating reward function from medial prefrontal cortex cortical activity using inverse reinforcement learning

208      Dynamic ensemble bayesian filter for robust BCI control of a human with tetraplegia

209      Modeling neural connectivity in a point-process analogue of Kalman filter

210      Utilizing goal-related information from medial prefrontal cortex in brain-machine interface

211      Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals: A case report

212      Epidural stimulation and robotic rehabilitation modulate muscle synergy patterns in rat hindlimb after spinal cord injury

213      Common and distinct effects of oxytocin on reinforcement learning under stable and volatile associations

214      Learning cross-frequency interactions in motor imagery decoding by interactive frequency convolutional neural networks

215      Experimental and simulation studies of localization and decoding of single and double dipoles

216      SPAIC: A spike-based artificial intelligence computing framework

217      Efficient point-process modeling of spiking neurons for neuroprosthesis

218      Motor imagery decoding in the presence of distraction using graph sequence neural networks

translational research

301      Design a novel BCI for neurorehabilitation using concurrent LFP and EEG features: a case study

302      Closed-loop deep brain stimulation for precise modulation of beta oscillations in parkinson’s disease

303      Mechanism of olfactory abnormalities in Shank3– mice — a comprehensive MRI-based study

304      Identification of potential MAPK-related key genes and regulation networks in molecular subtypes of major depressive disorder

305      Cortical representation of bimanual arm movements in the left primary motor cortex of a person with tetraplegia

306      Contextual cueing prevents retroactive interference in short-term perceptual training