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To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost. [ABSTRACT FROM AUTHOR]

In this letter, we evaluate antenna designs for ultra-high frequency and field (UHF) human brain magnetic resonance imaging (MRI) at 10.5 tesla (T). Although MRI at such UHF is expected to provide major signal-to-noise gains, the frequency of interest, 447 MHz, presents us with challenges regarding improved B1+ efficiency, image homogeneity, specific absorption rate (SAR), and antenna element decoupling for array configurations. To address these challenges, we propose the use of both monopole and dipole antennas in a novel hybrid configuration, which we refer to as a mono-dipole hybrid antenna (MDH) array. Compared to an 8-channel dipole antenna array of the same dimensions, the 8-channel MDH array showed an improvement in decoupling between adjacent array channels, as well as ∼18% higher B1+ and SAR efficiency near the central region of the phantom based on simulation and experiment. However, the performances of the MDH and dipole antenna arrays were overall similar when evaluating a human model in terms of peak B1+ efficiency, 10 g SAR, and SAR efficiency. Finally, the concept of an MDH array showed an advantage in improved decoupling, SAR, and B1+ near the superior region of the brain for human brain imaging. [ABSTRACT FROM AUTHOR]

Super-resolution three-dimensional (3-D) electromagnetic (EM) inversion for microwave human brain imaging is a typical high contrast EM inverse problem and requires huge computational costs. This work proposes a hybrid neural network electromagnetic inversion scheme (HNNEMIS) which contains shallow and deep neural networks to alleviate the required huge computational costs and solve this high contrast inverse problem. In the proposed scheme, semi-join back propagation neural network (SJ-BPNN) is employed to nonlinearly map the measured scattered electric field to two output channels, namely the permittivity and conductivity of scatterers, respectively. Such a semi-join strategy decreases the computational burden in training and testing processes. Then, a deep learning technique, termed U-Net, is employed to further enhance the imaging quality of the output from SJ-BPNN. To decrease the training cost and make neural networks fast convergent for human brain inversion, a novel training dataset construction strategy which contains the characteristics of human brain is also proposed. Noise-free and noisy numerical examples demonstrate that HNNEMIS has superior super-resolution inversion capabilities for human brain imaging. [ABSTRACT FROM AUTHOR]

The study of brain–computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications. [ABSTRACT FROM PUBLISHER]

Brain informatics (BI) is an emerging interdisciplinary and multidisciplinary research field that focuses on studying the mechanisms underlying the human information processing system. BI investigates the essential functions of the brain, ranging from perception to thinking, and encompassing such areas as multiperception, attention, emotion, memory, language, computation, heuristic search, reasoning, planning, decision making, problem solving, learning, discovery, and creativity. This special issue presents some of the best works being developed worldwide that deal with the new challenges of BI from an intelligent systems perspective. [ABSTRACT FROM PUBLISHER]

Hidden Markov models can be used to combine behavioral and brain-imaging data from an intelligent tutoring system to track mental states during student's problem-solving episodes. [ABSTRACT FROM PUBLISHER]