Towards the Design of BCI-based Accelerated Training System for Air Traffic Controllers

Communicated by Distinguished Professor Chin-Teng Lin 

May 2022

Chin-Teng Lin and Alka Rachel John

Humans are easily overwhelmed with tasks that push them beyond their capabilities. Despite their remarkable resilience to diverse working conditions, the work environment must be adapted to afford comfortable interactions with human operator abilities. Modern work environments position human operators at a supervisory level where they have extensive interactions with technology and must integrate multiple streams of information, demanding more cognitive resources and resulting in a higher workload in the human operators. 

Distributed Wireless Networks of Microimplants for Neural Recording and Stimulation

May 2022

Jihun Lee, Ahhyoung Lee, Vincent Leung, Farah Laiwalla, Arto Nurmikko

The concept of brain circuits computing as an extended network, composed of billions of neurons represents a contemporary view which is exploited in research of brain-machine interfaces (BMI). Population dynamics recorded from ensembles of neurons have been dominated by intracortical silicon-based microelectrode arrays (MEA), monolithic ‘beds of needles’, wired to external signal processing electronics. The work has deepened our understanding of underlying functional principles especially of the motor cortex as a network, leading to first clinical trials of human BMIs. The importance of computational techniques in neural decoding in this highly undersampled circumstance is demonstrated in the example study: e.g. recent work by the Stanford group where pattern recognition of spiking neural population has demonstrated a BMI hand writing-to-text capability. A forward-looking question is about the type of neural recording device technologies which are scalable and able to access a much larger number of neurons for decoding complex motor, sensory, and perhaps even cognitive tasks.

A Scalable and Power Efficient Retinal Prosthesis with an Optically-Addressed Nanoengineered Electrode Array

May 2022

Abraham Akinin, Jeremy M. Ford, Jiajia Wu, Chul Kim, Hiren D. Thacker, Patrick P. Mercier, and Gert Cauwenberghs.

Sight is integral to our ability to perceive and interact with the world. The visual system captures information in such detail that it encompasses almost the entire sensory input bandwidth of the brain. And yet, millions of patients are afflicted with blindness requiring assistive technologies and community accommodation.  A growing number of these cases are caused by diseases that result neural degeneration of photoreceptor cells in the retina such as Age-related Macular Degeneration.  Implantable prosthetics to electrically stimulate the retina and restore vision are an active area of academic research and commercialization efforts.  Unfortunately, considerable efforts have not produced a significant quality of life enhancement parallel to the astounding results of cochlear implants to restore hearing. To get there, novel approaches are needed to overcome the field’s main challenges: limited resolution and obtrusive packaging. 

New Opportunities of Soft Electronics in Biomedical Engineering

May 2022

Kuanming Yao, Guangyao Zhao, Xinge Yu

Distinguished from conventional rigid electronics, soft electronics is becoming a novel platform for next-generation biomedical instrumentations. With advanced materials, mechanics, and structural design, soft electronics could be realized in thin, light-weighted formats and thus can be worn on or implanted in human body, and may excel in great stretchability and conformal attachment with skin or tissue, which ensures continuous and precise healthcare monitoring or therapies. Our group focuses on exploring the novel soft electronics for the applications in various fields of biomedical applications, including motion and mechanical sensing, wearable energy harvesting, dynamic temperature sensing, sweat sensing, and closed-loop human-machine interface.

Neural Fragility of EEG May Help Localize the Seizure Onset Zone

May 2022

Adam Li, Chester Huynh, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson, Nathan Crone, Sara Inati, Kareem Zaghloul, Juan Bulacio, Jorge Gonzalez-Martinez, and Sridevi V. Sarma

Over 3.4 million people in the US have epilepsy and 30% of these patients have drug-resistant epilepsy (DRE), where they do not respond to medication. DRE patients are burdened by epilepsy-related disabilities and frequently hospitalized constituting around $13 billion dollars annually spent for treating epilepsy patients in the USA. Successful surgical treatment necessitates complete elimination of the brain region(s) known as the seizure onset zone (SOZ). Between 30%-70% of patients continue to have seizures 6 months after treatment due to mislocalization of the SOZ. We developed neural fragility, an electroencephalogram (EEG) marker for the SOZ, and validated it in a retrospective study of 91 patients predicting surgical outcomes using neural fragility conditioned on the clinically labeled SOZ. Fragility predicted 43 out of the 47 surgical failures correctly and had an overall accuracy of 76%, compared to the clinical accuracy of 48% (successful outcomes). Neural fragility outperformed 20 other EEG features on the same set of cross-validation samples suggesting it as a potential EEG biomarker for the SOZ.

Wireless Miniature Freely-Floating Optogenetic Stimulation Implant

Communicated by Dr. Jun Wang


December 2021

Linran Zhao, Wen Li, Maysam Ghovanloo, Yaoyao Jia

There is an increasing realization that the majority of brain functions relate to a large distributed network of neurons that are spread over different interconnected regions of the brain. Thus, neural recording and modulation of the future will require the ability to simultaneously interface with multiple neural sites distributed over a large brain area. Traditional methods for modulating neuronal function have relied on direct stimulation by tiny electrodes, which effectiveness is undermined by the limited spatial and temporal precision with which individual cells can be selectively targeted. The emergence of optogenetic stimulation provides distinct advantages over electrical stimulation, such as cell-type specificity, sub-millisecond temporal precision, and rapid reversibility. Optogenetic neuromodulation has the potential to revolutionize the study of how neurons operate as members of larger networks and may ultimately help patients suffering from neurological disorders. Hence, we aspire to design a distributed wireless neural interface framework to stimulate large-scale neuronal ensembles over large brain areas. The distributed framework includes an array of tiny, wireless, and highly efficient implants, each of which operates autonomously to stimulate neural activities.

Spherical Biomimetic Eyes with Nanowire Arrays: From design to application

Communicated by Dr. Yiwen Wang


December 2021

Leilei Gu, Yucheng Ding, Zhiyong Fan

“To see is to believe”. High-performance imaging devices are essential in society, particularly in the current age of Artificial Intelligence (AI) +. The biological eyes have been polished by natural selection for millions of years and their function has been verified by the diverse environment. Learning from the masterpiece of nature is therefore a shortcut to improve our manmade systems. As one of the wisest creatures in nature, human eyes are advanced image sensing systems with superiorities such as high resolution, wide field-of-view (FoV), high energy efficiency, and strong accommodations. Their high performance originates from the combined effect of a vastly flexible optical system, high-density and sensitive photoreceptor arrays, and powerful neural networks from both retina and cortex. The human eyes have a spherical shape with a hemispherical retina. A hemispherical shape matches well with the Petzval surface, which is the theoretical focal plane of the spherical lens, leading to clear and sharp imaging. In regular cameras, to mitigate the mismatching in planar structure, a delicate lens array has to be inserted to gradually bend the focal plane into quasi-flat. In our cell phones, there are 10-16 lens. With a well-designed hemispherical image sensor, high-quality imaging with a simple structure can be achieved.

Beneficial Perturbation Network for Lifelong Learning

Communicated by Dr. Yuxiao Yang


December 2021

Shixian Wen, Laurent Itti

Lifelong learning challenges

The human brain can quickly learn and adapt its behavior in a wide range of environments throughout its lifetime. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs.  In more complex and dynamic scenarios where the inputs to outputs mapping may change with different contexts, the deployment of these deep neural network systems would be constrained. One of the failed salient scenarios is lifelong learning—learning new independent tasks sequentially without forgetting previous tasks. More specifically, agents should incrementally learn and evolve based on multiple tasks from various data distributions across time while remembering previously learned knowledge. In general, current neural networks are not capable of lifelong learning and usually suffer from “catastrophic forgetting”—learning the knowledge of the new task would overwrite the fixed learned mapping of an old task. This effect typically leads to a significant decrease of the network performances on previous tasks or, in the worst case, leads to the network completely forgetting all previous tasks.

Brain-inspired cognitive model with attention for self-driving cars

Communicated by Dr. Sung-Phil Kim


December 2021

Shitao Chen, Songyi Zhang, Badong Chen and Nanning Zheng

As a typical artificial intelligence system, self-driving cars, unlike normal artificial intelligence systems, usually concern the safety of people’s lives and property, and have little tolerance of mistakes. With the furthering of research on self-driving technology, the existing computing framework based on the “perception-planning-decision-control” information processing method has increasingly manifested the problems of low computing efficiency, poor environmental adaptability, and insufficient self-learning ability. Our research work mainly refers to the psychological level of human cognition to construct a new type of self-driving method.

Cross-Cultural Exploration of Neuroethics in Engineering


December 2021

Cynthia Weber, PhD, on behalf of IEEE Brain

Guidelines that consider societal and cultural impacts of neurotechnology are crucial for ensuring responsible innovation in the field.

Ethical considerations have not always been of primary concern in the development of technology. However, the need for ethical standards and guidelines for neurotechnology has received significant support with multiple efforts underway that aim to sidestep past mistakes by preparing for future development and use cases. The challenge lies in identifying the complex social, legal, and cultural issues tied to how neurotechnologies will be accessed and implemented once released into the world, and the associated safety, privacy, and long-term consequences of its use. For many people, the brain is intimately connected to one’s sense of self and personal identity—our thoughts and emotions, for example. Consequently, neurotechnology devices that intervene with the brain, whether for medical treatment, wellness applications, or entertainment, may pose unique perceived risks for the user. This is also the case when neurotechnology has the potential to be implemented in employment, legal, or educational contexts. In all these scenarios, ethical considerations are interwoven within layers of consent, data access and control, and possible manipulation.