Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCIs. Over the past decade, a wide range of BCI applications with AI assistance have emerged. These "smart" BCIs including motor and sensory BCIs have shown notable clinical success, improved the quality of paralyzed patients' lives, expanded the athletic ability of common people and accelerated the evolution of robots and neurophysiological discoveries. However, despite technological improvements, challenges remain with regard to the long training periods, real-time feedback, and monitoring of BCIs. In this article, the authors review the current state of AI as applied to BCIs and describe advances in BCI applications, their challenges and where they could be headed in the future.

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The precision of local oscillator (LO) signal in in-phase and quadrature (IQ) demodulation strongly affects the imaging performance of millimeter wave (mmWave) radars. Therefore, to eliminate the requirement for high-precision LO, a simple yet effective digital IQ demodulation method has been proposed with the aid of a specified sampling scheme in order to eliminate the demand for LO. Based on the bandpass sampling theorem, the characteristic of the intermediate frequency signal of mmWave imaging indicates that the LO is unrequired if the sampling rate is twice of the frequency of the carrier of the intermediate signal. In this way, the in-phase signal would be directly and accurately obtained by performing the Binary-Phase-Shift-Keying (BPSK) modulation on the samples, based on which the IQ demodulation would be completed by using the Hilbert transform. The proposed method does not employ LO and thus simplifies the demodulation process and is suitable for implementation in a Field-Programmable Gate Array (FPGA) with fewer hardware resources. To verify the method, a three-dimensional mmWave radar imaging is carried out at the 30-34 GHz bandwidth, where the sampling and digital IQ demodulation are realized by an ADC (AD9250) and FPGA (XC7K325T), respectively. The results show a simplified transceiver with lower requirements and the prospect of the proposed method being applied in radar imaging and other related fields.