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Micro- and nano-technology has enabled development of smaller and smarter wearable devices for medical and lifestyle related applications. In particular, recent advances in EEG monitoring technologies pave the way for wearable, wireless EEG monitoring devices. Here, a low-power wireless EEG sensor platform that measures 8-channels of EEG, is described. The(More)
The European project NeuroProbes has introduced a new methodology to allow the fine positioning of electrodes within an implantable probe with respect to individual neurons. In this approach, probes are built with a very large number of electrodes which are electronically selectable. This feature is implemented thanks to the modular approach adopted in(More)
This paper discusses ultra-low-power wireless sensor nodes intended for wearable biopotential monitoring. Specific attention is given to mixed-signal design approaches and their impact on the overall system power dissipation. Examples of trade-offs in power dissipation between analog front-ends and digital signal processing are also given. It is shown how(More)
An ECG signal processor (ESP) is proposed for the low energy wireless ambulatory arrhythmia monitoring system. The ECG processor mainly performs filtering, compression, classification and encryption. The data compression flow consisting of skeleton and modified Huffman coding is the essential function to reduce the transmission energy consumption and the(More)
This paper presents multi-electrode arrays for in vivo neural recording applications incorporating the principle of electronic depth control (EDC), i.e., the electronic selection of recording sites along slender probe shafts independently for multiple channels. Two-dimensional (2D) arrays were realized using a commercial 0.5- μm(More)
We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can " mind-type " text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a simple classifier which relies on a linear feature extraction approach. The accuracy of the(More)