Refet Firat Yazicioglu

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This paper gives an overview of results of the Human++ research program [1]. This research aims to achieve highly miniaturized and nearly autonomous sensor systems that assist our health and comfort. It combines expertise in wireless ultra-low power communications, packaging and 3D integration technologies, MEMS energy scavenging techniques and low-power(More)
This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance(More)
This paper proposes a 3-channel biopotential monitoring ASIC with simultaneous electrode-tissue impedance measurements which allows real-time estimation of motion artifacts on each channel using an an external μC. The ASIC features a high performance instrumentation amplifier with fully integrated sub-Hz HPF rejecting rail-to-rail electrode-offset voltages.(More)
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)
This paper gives an overview of the results of IMEC's Human++ research program [1]. This program aims to achieve highly miniaturized and autonomous sensor systems that enable people to carry their personal body area network. The body area network will provide medical, lifestyle, assisted living, sports or entertainment functions. It combines expertise in(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)
This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal(More)