Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

[1]  Andre van Schaik,et al.  Dry electrode bio-potential recordings , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[2]  P. Bifulco,et al.  A wearable device for recording of biopotentials and body movements , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[3]  Ning Wang,et al.  Pregnancy detection and monitoring in cattle via combined foetus electrocardiogram and phonocardiogram signal processing , 2012, BMC Veterinary Research.

[4]  Maria Romano,et al.  An ultra-high input impedance ECG amplifier for long-term monitoring of athletes , 2010, Medical devices.

[5]  C. Grozea,et al.  Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications , 2011, Journal of neural engineering.

[6]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[8]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  P. Bifulco,et al.  A mobile EEG system with dry electrodes , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[10]  Gaetano D. Gargiulo,et al.  True Unipolar ECG Machine for Wilson Central Terminal Measurements , 2015, BioMed research international.

[11]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[12]  Tauseef Gulrez,et al.  Brain-computer interface: Next generation thought controlled distributed video game development platform , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

[13]  James C. Christensen,et al.  Validation of a Dry Electrode System for EEG , 2009 .

[14]  André van Schaik,et al.  Non-invasive Electronic Biosensor Circuits and Systems , 2010 .

[15]  Gaetano Gargiulo Portable bio-signals devices for brain computer interface and long-term patient monitoring , 2010 .

[16]  Paolo Bifulco,et al.  Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study , 2012, Medical devices.

[17]  P Bifulco,et al.  Towards true unipolar ECG recording without the Wilson central terminal (preliminary results) , 2013, Physiological measurement.

[18]  Barrett C. Craner,et al.  Design and Development of Medical Electronic Instrumentation , 2005 .

[19]  Maria Romano,et al.  A 9-independent-leads ECG system from 10 electrodes: A practice preserving WCT-less true unipolar ECG system , 2015, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[20]  Ruei-Cheng Wu,et al.  Applications of event-related-potential-based brain computer interface to intelligent transportation systems , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[21]  Tzyy-Ping Jung,et al.  A brain-machine interface using dry-contact, low-noise EEG sensors , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[22]  K. Müller,et al.  Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes , 2007, PloS one.

[23]  M. Aminoff Evoked potentials in clinical medicine. , 1986, The Quarterly journal of medicine.

[24]  G·加希乌洛 A system for sensing electrophysiological signals , 2009 .

[25]  M. Teplan FUNDAMENTALS OF EEG MEASUREMENT , 2002 .

[26]  Andre van Schaik,et al.  Wearable dry sensors with bluetooth connection for use in remote patient monitoring systems. , 2010, Studies in health technology and informatics.

[27]  Jaroslaw Zygierewicz,et al.  On the statistical significance of event-related EEG desynchronization and synchronization in the time-frequency plane , 2004, IEEE Transactions on Biomedical Engineering.

[28]  R. Granit THE HEART ( Extract from “ Principles and Applications of Bioelectric and Biomagnetic Fields , 2005 .

[29]  André van Schaik,et al.  A new EEG recording system for passive dry electrodes , 2010, Clinical Neurophysiology.

[30]  André van Schaik,et al.  Giga-Ohm High-Impedance FET Input Amplifiers for Dry Electrode Biosensor Circuits and Systems , 2011 .

[31]  Paolo Bifulco,et al.  Towards true unipolar bio-potential recording: a preliminary result for ECG. , 2013, Physiological measurement.

[32]  Vojkan Mihajlovic,et al.  To What Extent can Dry and Water-based EEG Electrodes Replace Conductive Gel Ones? - A Steady State Visual Evoked Potential Brain-computer Interface Case Study , 2011, BIODEVICES.

[33]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[34]  H. Nakano,et al.  Analysis of the components of electrically evoked response using a monopolar recording technique. , 1993, Investigative ophthalmology & visual science.

[35]  Maria Romano,et al.  Problems in Assessment of Novel Biopotential Front-End with Dry Electrode: A Brief Review , 2014 .

[36]  Robert T. Knight,et al.  An active, microfabricated, scalp electrode array for EEG recording , 1996 .

[37]  John G. Webster,et al.  Reductionl of Interference Due to Common Mode Voltage in Biopotential Amplifiers , 1983, IEEE Transactions on Biomedical Engineering.

[38]  Paolo Bifulco,et al.  Open platform, 32-channel, portable, data-logger with 32 pga control lines for wearable medical device development , 2014 .

[39]  W. Graham Richards,et al.  Art of electronics , 1983, Nature.

[40]  Michael Bach,et al.  Guidelines for calibration of stimulus and recording parameters used in clinical electrophysiology of vision , 2003, Documenta Ophthalmologica.

[41]  André van Schaik,et al.  Unipolar ECG circuits: Towards more precise cardiac event identification , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[42]  P. Bifulco,et al.  Mobile biomedical sensing with dry electrodes , 2008, 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[43]  Paolo Bifulco,et al.  Non Invasive Foetal Monitoring with a Combined ECG - PCG System , 2011 .

[44]  E. Waterhouse,et al.  New horizons in ambulatory electroencephalography , 2003, IEEE Engineering in Medicine and Biology Magazine.

[45]  Orazio Aiello,et al.  Instrumented flexible active electrode matrix suitable for human-computer interface applications , 2016 .

[46]  Jerry Y. H. Fuh,et al.  Micro-spike EEG electrode and the vacuum-casting technology for mass production , 2009 .

[47]  F Cincotti,et al.  Current trends in hardware and software for brain–computer interfaces (BCIs) , 2011, Journal of neural engineering.

[48]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.