Abstract. RFID applications do not always use encryption to ensure the security as public key cryptographic algorithms that are costly in term of computing resources. We proposed to secure the communication from an ISO 14443 RFID card to the reader against passive eavesdropping at the physical layer by adding noise. During the card reply, the reader generates an appropriate noise that is modulated by the load of the card. By knowing the noise it emitted, the reader is able to subtract it and to retrieve the card message when a spying probe in the field is inefficient.

A wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE(®) has been designed for clinical applications. This active implantable medical device is able to record ECoG on 64 electrodes with selectable gain and sampling frequency, with less than 0.7 µVRMS input referred noise in the [0.5 Hz - 300 Hz] band. It is powered remotely through an inductive link at 13.56 MHz, communicates wirelessly on the MICS band at 402-405 MHz with a custom designed base station connected to a PC and complies with the regulations applicable to class III AIMD. The design of the housing and the antenna have been optimized to ease the surgery and to take into account all the requirements of a clinical trial in particular patient safety and comfort. The main features of this WIMAGINE(®) implantable device and its architecture will be presented, as well as its performances and in vivo validations.

A wireless, low power, 64-channel data acquisition system named WIMAGINE has been designed for ElectroCorticoGram (ECoG) recording. This system is based on a custom integrated circuit (ASIC) for amplification and digitization on 64 channels. It allows the RF transmission (in the MICS band) of 32 ECoG recording channels (among 64 channels available) sampled at 1 kHz per channel with a 12-bit resolution. The device is powered wirelessly through an inductive link at 13.56 MHz able to provide 100mW (30mA at 3.3V). This integration is a first step towards an implantable device for brain activity monitoring and Brain-Computer Interface (BCI) applications. The main features of the WIMAGINE platform and its architecture will be presented, as well as its performances and in vivo studies.