WiBi: Wireless Biology

Figure 1: Concept for Fully Integrated Wireless Brain-Machine Interface Chip

Figure 1: Concept for Implantable Wireless Neural Interface

In the past few years implanted brain-machine interfaces (BMI) have show significant promise. The immediate impact of this technology is to enable direct cortical recording to better understand how the brain functions. In the long term our research builds technology that could enhance the quality of life of quadriplegics and people with debilitating diseases. Ideally these devices have completely wireless operation, making any wires through the skin obsolete to reduce any risk of infection while increasing the patient`s comfort. The potential impact of such interfaces extends from advanced prosthetics over micro-stimulation for treatment of certain neurological illnesses to completely new user-computer interfaces. The purpose of this project is to implement novel building blocks for signal acquisition, data communication and power transfer exploiting the advantages of a deeply scaled CMOS process.

System Components:

1. Power and Area Efficient Signal Acquisition Front-End

Figure 2: Neural Front-End with Low-Power Digital Filtering

Figure 2: Neural Front-End with Low-Power Digital Filtering

The signals of interest, action and local field potentials, run at biological timescales in the range of 1–10 kHz and can be as small as 10uV, which presents the challenge of low signal to noise ratio (SNR) at the sensor input. Thus a low-power low-noise signal acquisition front-end is critical to any brain-machine sensor array interface to amplify and convert the neural data for digital signal processing. A significant amount of work has been devoted to this problem over the last decade; however, the prior art has relied heavily on analog techniques and passives to perform signal conditioning and filtering, which significantly impacts die area and does not result a scalable solution. We propose an architecture in a fine-line process, which uses feedback from the digital domain to set filter pole locations thereby eliminating the need for the integration of large passive components. Combining over-sampling acquisition and digital signal processing in a reconfigurable system can result in significant implant power and area reduction.

Students: Rikky Muller, Simone Gambini

2. Ultra Low Power Wireless for Biomedical Implants

Figure 2: Reflective Impulse Radio Architecture

Figure 3: Reflective Impulse Radio Architecture

Research in recent years has demonstrated the possibility of deploying wireless technology in a number of sensor networks [1]. From the perspective of the communication link, a class of these applications possesses extreme asymmetry between receiver and transmitter. While the receiver is allowed to have higher power and larger size, the transmitter (a.k.a. transponder) is typically an integrated part of the sensor node and is subject to stringent power and size constraints. Examples of such applications include automotive and biomedical implants.

This project seeks to explore the design opportunities presented by link asymmetry. While the receiver is allowed to have higher power and larger size, the transmitter is an integrated part of the sensor node and is subject to stringent power and size constraints. Required data rate, on the other hand, is on the order of Mbps. To explore the opportunities presented by link asymmetry, design parameters at both system and circuit levels are carefully studied. The objective is to trade spectral efficiency for power efficiency, to operate the transmitter circuits with heavy duty cycling, and to leverage techniques already employed in passive RFID transponders.

Student: David Chen

3. Powering Wireless Implanted Brain-Machine Interfaces

A challenge that is often overlooked is how to power wireless implantable devices. The size of an implant is ideally dictated by the size of the electrode array, which can vary between 1 – 100 mm², depending on the application and kind of electrode. The goal of this research is to determine the maximum available power for a given overall size constraint in the range of 1×1 – 10×10 mm² and build systems in a fine-line CMOS process that can deliver this power to the implant.

Student: Michael Mark

Collaborators

References

[1] J. Rabaey, et al., “PicoRadios for Wireless Sensor Networks: The Next Challenge in Ultra-Low Power Design”, ISSCC Digest of Technical Papers, pp. 200-201, Feb. 2002