Literature DB >> 19164053

Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes.

R Matthews1, P J Turner, N J McDonald, K Ermolaev, T Manus, R A Shelby, M Steindorf.   

Abstract

This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.

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Year:  2008        PMID: 19164053     DOI: 10.1109/IEMBS.2008.4650550

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  8 in total

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Review 2.  Dry EEG electrodes.

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6.  Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers.

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7.  Single-trial classification of gait and point movement preparation from human EEG.

Authors:  Priya D Velu; Virginia R de Sa
Journal:  Front Neurosci       Date:  2013-06-11       Impact factor: 4.677

8.  A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

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  8 in total

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