A Minimally Invasive 64-Channel Wireless mu ECoG Implant

Emerging applications in brain–machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that overcomes these difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex. The device is comprised of a highly flexible, high-density, polymer-based 64-channel electrode array and a flexible antenna, bonded to 2.4 mm × 2.4 mm CMOS integrated circuit (IC) that performs 64-channel acquisition, wireless power and data transmission. The IC digitizes the signal from each electrode at 1 kS/s with 1.2 μV input referred noise, and transmits the serialized data using a 1 Mb/s backscattering modulator. A dual-mode power-receiving rectifier reduces data-dependent supply ripple, enabling the integration of small decoupling capacitors on chip and eliminating the need for external components. Design techniques in the wireless and baseband circuits result in over 16× reduction in die area with a simultaneous 3× improvement in power efficiency over the state of the art. The IC consumes 225 μW and can be powered by an external reader transmitting 12 mW at 300 MHz, which is over 3× lower than IEEE and FCC regulations. Manuscript received April 28, 2014; revised August 03, 2014, September 12, 2014; accepted October 14, 2014. This paper was approved by Guest Editor David Stoppa. R.Muller is with the Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 USA, and also with Cortera Neurotechnologies Inc., Berkeley, CA 94704 USA. H. -P. Le is with the Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 USA, and also with Lion Semiconductor Inc., Berkeley, CA 94720 USA. W. Li is with the Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 USA. P. Ledochowitsch is with the Department of Bioengineering, University of California, Berkeley, CA 94720 USA and University of San Francisco, San Francisco, CA 94143 USA, and also with the Allen Institute for Brain Science, Seattle, WA 98103 USA. S. Gambini is with San Francisco, CA 94103 USA. T. Bjorninen is with Tampere University of Technology, FI-33101 Tampere, Finland. A. Koralek is with the HellenWills Neuroscience Institute, University of California, Berkeley, CA 94720 USA. J. Carmena is with the Department of Electrical Engineering and Computer Science and the Hellen Wills Neuroscience Institute, University of California, Berkeley, CA 94720 USA. M. Maharbiz is with the Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA USA, and also with the Department of Bioengineering, University of California, Berkeley, CA 94720USA and University of San Francisco, San Francisco, CA 94143 USA. E. Alon, and J. Rabaey are with the Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee. org. Digital Object Identifier 10.1109/JSSC.2014. 2364824