Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human’s cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding.

Metamaterials and metasurfaces have inspired worldwide interest in the recent two decades due to their extraordinary performance in controlling material parameters and electromagnetic properties. However, most studies on metamaterials and metasurfaces are focused on manipulations of electromagnetic fields and waves, because of their analog natures. The concepts of digital coding and programmable metasurfaces proposed in 2014 have opened a new perspective to characterize and design metasurfaces in a digital way, and made it possible to control electromagnetic fields/waves and process digital information simultaneously, yielding the birth of a new direction of information metasurfaces. On the other hand, artificial intelligence (AI) has become more important in automatic designs of metasurfaces. In this review paper, we first show the intrinsic natures and advantages of information metasurfaces, including information operations, programmable and real-time control capabilities, and space-time-coding strategies. Then we introduce the recent advances in designing metasurfaces using AI technologies, and particularly discuss the close combinations of information metasurfaces and AI to generate intelligent metasurfaces. We present self-adaptively smart metasurfaces, AI-based intelligent imagers, microwave cameras, and programmable AI machines based on optical neural networks. Finally, we indicate the challenges, applications, and future directions of information and intelligent metasurfaces.

Programmable metasurfaces allow real-time electromagnetic (EM) manipulation in a digital manner, showing great potential to construct advanced multifunctional EM devices. However, the current programmable metasurfaces typically need human participation to achieve various EM functions. In this Letter, we propose, design, and construct a self-adaptive metasurface platform that can achieve beam control automatically based on image recognition. Such a platform is composed of a metasurface with 36 × 36 active units, a single-board computer, and two independent cameras that can detect the position of the objects. The single-board computer, Raspberry Pi, is used to calculate the information of the objects and generate the coding sequences to control the digital metasurface based on a low complexity binocular localization algorithm. Such a smart metasurface platform can generate different beams according to the location of the receiver and can be used as intelligent antennas in future communications and radars.

scale, they are considered as alternative approaches towards the radio frequency (RF) front-end design to realize beamforming and scanning in wireless communication systems. [8][9][10] In the past decades, several major breakthroughs have boosted the research of metamaterials significantly. [11] For instance, active components were introduced to the metasurfaces to realize dynamic controls of EM waves. In 2014, Cui et al. proposed the concept of programmable digital coding metasurfaces, bridging the digital world to the EM physical world for the first time. [12][13][14] Following the footstep of information society and booming of artificial intelligence, the focus of the latest researches around the metasurfaces has shifted to autonomous perception, self-adaptation, and high-performance computing in the EM fields. [15][16][17][18][19][20] Today's data-driven society has witnessed a pressing demand for high-resolution and rapid-response imaging system. However, the traditional microwave imagers require very bulky and repetitive circuit components, restraining their applications in cost-sensitive and deployable situations. Combined with the superior manipulation of the EM wave and compact dimensions, the metasurface-based microwave imagers have been proposed in recent years and exhibited excellent performance in the image reconstruction and processing. For example, a linear EM model for the reflective-metasurface-based microwave imager with multiple measurements was presented. [21] Based on this model, a frequency-multiplexing metasurface-based microwave imager was designed for security check, where a passive metasurface was used as the EM wave reflector and the parameters at several frequency points were collected as the raw data. [22] However, the quality of reconstructed images suffers from severe deterioration when the noise level increases. Also, compressed sensing algorithm was employed to reconstruct the images from the scattered signals under random scattering pattern iteratively, which inevitably increases the complexity of system. [23,24] Other novel methods have also been proposed to solve the inverse problems in EM imaging, including artificial neural networks, convolution neural networks, which are all limited in the digital domain implemented in the postprocessing module. [25][26][27] In 2014, a terahertz compressive imaging system was proposed using metamaterial spatial light modulators. [28] With the introduction of active metasurfaces, the incident EM beam was manipulated artificially with improved signal-to-noiseIn the data-driven society, fidelity and accuracy of automatic decisions behind the scene rely fundamentally on a solid data or imaging acquisition system. However, conventional microwave imagers are inadequate relating to their resolution and noise capability, mainly due to the limited aperture size and rigid working principle. Here, a programmable metasurface imager with highresolution and anti-interference performance is proposed. By implementing the structure of multilayer perceptron n...

Recently, some new forms of arti cial intelligence computing hardware and chips have been presented. However, most of them have di culties to simultaneously achieve advantages of light-speed computing, programmable weight matrix, and programmable nonlinear activation functions. Here, we propose a programmable surface plasmonic neural network (SPNN) with programmable weights and activation functions based on a spoof surface plasmon polariton (SSPP) platform, which can perform intelligent functions and sense electromagnetic (EM) waves at nearly light speed. We demonstrate a parallel coupling SSPP structure loaded with varactors to introduce four paths with tunable transmitting parameters. On this multi-port architecture, we further establish a real-time control and feedback method to enable arbitrarily designable activation functions under a detecting feedback loop. Experimental results show that a four-in and four-out fully-connected super-neuron can ful ll independently adjustable weights and programmable activation functions, where each input can be sensed for arbitrarily programming. To comprehensively show the above capabilities, we design and demonstrate experimentally a wireless communication system based on the SPNN for image decoding and recovery.We further illustrate a partially connected SPNN using the super-neurons with a high prediction accuracy. The proposed concept paves a new way for arti cial intelligence devices, stimulating the fascinating elds like large-scale EM computing and communication systems in the future.