Literature DB >> 21096892

A low-power, wireless, 8-channel EEG monitoring headset.

Lindsay Brown1, Jef van de Molengraft, Refet Firat Yazicioglu, Tom Torfs, Julien Penders, Chris Van Hoof.   

Abstract

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 platform is integrated into a wearable headset for ambulatory monitoring of EEG. While using standard EEG electrodes without conductive gel, a first evaluation shows the wireless headset is comparable to the reference system when looking at alpha wave discrimination. This device combines low-noise, and low-power functionality into an easy-to-use wireless headset, providing a first step towards a fully integrated, fully functional wearable wireless EEG monitoring system.

Mesh:

Year:  2010        PMID: 21096892     DOI: 10.1109/IEMBS.2010.5627393

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  6 in total

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3.  Diagnostic and therapeutic yield of a patient-controlled portable EEG device with dry electrodes for home-monitoring neurological outpatients-rationale and protocol of the HOMEONE pilot study.

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Journal:  Pilot Feasibility Stud       Date:  2018-05-21

4.  Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications.

Authors:  Hermann Hinrichs; Michael Scholz; Anne Katrin Baum; Julia W Y Kam; Robert T Knight; Hans-Jochen Heinze
Journal:  Sci Rep       Date:  2020-03-23       Impact factor: 4.379

5.  Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces.

Authors:  Francisco Laport; Daniel Iglesia; Adriana Dapena; Paula M Castro; Francisco J Vazquez-Araujo
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6.  A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

Authors:  Hendrik Wöhrle; Marc Tabie; Su Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
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  6 in total

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