The IEEE Brain Initiative eNewsletter is a quarterly online publication launched in January 2017. It features practical and timely information and forward-looking commentary on neurotechnologies and neuroengineering. eNewsletter articles can describe recent breakthroughs in research, primers on methods of interests, or report recent events such as conferences or workshops. You can contact the eNewsletter editor with any questions concerning the topic or content of your article.
Communicated by Distinguished Professor Chin-Teng Lin
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.
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.
Communicated by Dr. Yuxiao Yang
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.
Communicated by Dr. Sung-Phil Kim
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.
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.
The COVID19 pandemic has brought upon us an unprecedented situation. The current events have had a profound impact at a global scale and have once more shown the important role that science and technology play in providing solutions to societal challenges.
Laura M. Ferrari1*, E. Ismailova3*, F.Greco2*
Temporary Tattoo Electrodes (TTEs) are dry and conformable electrodes that are able to capture weak surface electrophysiological signals while being imperceptible for the user. We demonstrated the use and characterised TTEs in a clinical electroencephalography (EEG) monitoring set-up, proving for the first time the compatibility of a dry electrode with magnetoencephalography (MEG) sensors (Figure 1) (1).