Near-field magnetic wireless systems have distinct advantages over their conventional far-field counterparts in water-rich environments, such as underwater, underground, and in biological tissues, due to lower power absorption. This paper presents a comprehensive review of near-field magnetic wireless power transfer (WPT) and communication technologies in a variety of applications from general free-space systems, to implantable biomedical devices we find of particular interest. To implement a fully wirelessly-powered implantable system, both high-efficiency power transfer and high-rate data communication are essential. This paper first presents the history and the fundamentals of near-field WPT and communication in free-space systems, followed by technical details for their specific use in implantable biomedical devices. Finally, this paper reviews recent advances in simultaneous wireless information and power transfer (SWIPT) and highlights their applications in implantable biomedical systems. The knowledge reviewed in the paper could provide intuition in the design of various wireless and mobile systems such as wireless body area networks, small-cell 5G cellular, as well as in-body biomedical applications, especially for efficient power and data management and higher security.

The aim of this work is to develop an effective brain–computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s⁻¹ of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

Cognitive decline (CD) is a major symptom of mild cognitive impairment (MCI). Patients with MCI have an increased likelihood of developing Alzheimer’s disease (AD). Although a cure for AD is currently lacking, medication therapies and/or daily training in the early stage can alleviate disease progression and improve patients’ quality of life. Accordingly, investigating CD-related biomarkers via brain imaging devices is crucial for early diagnosis. In particular, “portable” brain imaging devices enable frequent diagnostic checks as a routine clinical tool, and therefore increase the possibility of early AD diagnosis. This study aimed to comprehensively investigate functional connectivity (FC) in the prefrontal cortex measured by a portable functional near-infrared spectroscopy (fNIRS) device during a working memory (WM) task known as the delayed matching to sample (DMTS) task. Differences in prefrontal FC between healthy control (HC) (n = 23) and CD groups (n = 23) were examined. Intra-group analysis (one-sample t-test) revealed significantly greater prefrontal FC, especially left- and inter-hemispheric FC, in the CD group than in the HC. These observations could be due to a compensatory mechanism of the prefrontal cortex caused by hippocampal degeneration. Inter-group analysis (unpaired two-sample t-test) revealed significant intergroup differences in left- and inter-hemispheric FC. These attributes may serve as a novel biomarker for early detection of MCI. In addition, our findings imply that portable fNIRS devices covering the prefrontal cortex may be useful for early diagnosis of MCI.

Dry transfer printing by using atomic diffusion control enables multiscale and deterministic assembly of electronic devices.

By monitoring brain neural signals, neural recorders allow for the study of neurological mechanisms underlying specific behavioural and cognitive states. However, the large brain volumes of non-human primates and their extensive range of uncontrolled movements and inherent wildness make it difficult to carry out covert and long-term recording and analysis of deep-brain neural signals. Here we report the development and performance of a stealthy neural recorder for the study of naturalistic behaviours in non-human primates. The neural recorder includes a fully implantable wireless and battery-free module for the recording of local field potentials and accelerometry data in real time, a flexible 32-electrode neural probe with a resorbable insertion shuttle, and a repeater coil-based wireless-power-transfer system operating at the body scale. We used the device to record neurobehavioural data for over 1 month in a freely moving monkey and leveraged the recorded data to train an artificial intelligence model for the classification of the animals’ eating behaviours.