Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

This paper introduces a 2.4 GHz down-conversion quadrature mixer which applied in the Wireless Sensor Network (WSN). The mixer uses a folded structure which is modified based on the conventional Gilbert mixer. It is designed in 0.18μm RF CMOS process with a low supply voltage of 1V. The post-simulation results show that the mixer achieves a conversion gain (CG) of 9.0dB, the input 1dB compression point (IP1dB) of-7.6dBm, the third-order input intercept point (IIP3) of 2.2dBm, and the single side-band (SSB) noise figure (NF) is 13.9dB. The mixer core consumes current about 1.2mA from a 1V power supply.

A fourth-order low-pass continuous-time filter for a WSN transmitter is presented. The active RC filter was chosen for the high linearity, designed by using the leapfrog topology imitates the passive filter. The operation amplifier (op-amp) adopted by the filter is feed-forward operation amplifier, which could get the GBW as large as possible under the low power consumption. The cut-off frequency deviation due to the process corner, aging and temperature deviation is adjusted by an automatic frequency tuning circuit. The filter in a 0.18μm RF CMOS technology consumes 1mW from a 1V power supply. The measured results of the chip show that the bandwidth is about 1.5MHz. The voltage gain of filter is about-4.5dB with the buffer, the ripple in the pass-band is lower than 0.5 dB, and the channel rejection ratio is larger than 30dB at 4MHz.

A low voltage, low power up-conversion mixer is presented here for 2.4GHz wireless sensor networks (WSN). It was based on a double-balanced Gilbert cell type. The current-reuse technique was used to reduce the power consumption and negative-resistance compensation technique was used to improve the conversion gain. The mixer was designed in 0.18μm RF CMOS technology, and was simulated with Cadence SpectreRF. The simulation results indicate that the conversion gain is 6.37dB, the noise figure is 15.36dB and the input 1dB compression point is-10.3dBm, while consuming 1mA current for operating voltage at 1V.

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