Electroencephalo-graph signal spike potential detection circuit based on nonlinear energy operator and construction method thereof

Abstract

The invention discloses an electroencephalo-graph signal spike potential detection circuit based on a nonlinear energy operator and a construction method thereof. The detection circuit comprises two total difference differential amplifiers, two four-quadrant current mode analog multipliers and a current signal subtractor; first-order time domain differential signals and a second-order time domain differential signal of an original input signal are obtained by the total difference differential amplifiers, and a square signal of two first-order time domain differential signals and the product signal of the original input signal and the second-order time domain differential signal are obtained by the two analog multiplier; and the difference between the output signals of the two analog multipliers is obtained by the current signal subtractor, and thus, the spike potential of an electroencephalo-graph signal is obtained. The construction method of the detection circuit is implemented mainly by employing complementary metal-oxide-semiconductor transistor (CMOS) translinear loops in the four-quadrant current mode multipliers and setting the working circuits of the total difference differential amplifiers in a subthreshold area. In the invention, four-quadrant analog multipliers are implemented by very small static power consumption, the area of chips is saved, and the system power consumption is lowered.

Description

technical field [0001] The invention relates to the technical field of implantable cranial nerve signal peak potential detection integration, in particular to a brain electric peak potential detection circuit based on non-linear energy calculation in a subthreshold region and a construction method thereof. Background technique [0002] Implantable wireless EEG acquisition usually adopts a multi-electrode signal acquisition method. Since multi-channel peak potential signals are obtained at the same time, interactive detection and processing can be completed. However, the signals collected by different electrodes often contain the information of the surrounding electrodes and a large amount of background noise. The signal of each channel needs to be post-processed and A / D converted, so a large amount of redundant information makes the post-processing of the system and the wireless transmission of data amount increased. At the same time, because wireless communication has limi...

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Problems solved by technology

Area window detection and wavelet transform applications have high algorithm complexity, are not easy to realize hardware integration, and cannot meet the needs of implantable brain-computer interfaces with high speed and low power consumption in a small volume

Although the threshold comparison detection is easy to integrate and realize, the shortcomings of low

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