Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements

The aim of this study was to develop a novel dry electroencephalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull, providing a suitable surface contact area for collecting electrical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biosignal measurements, we proposed a new, augmented wire-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, this augmented WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is feasible for practical applications.

[1]  Shao-Wei Lu,et al.  Design, Fabrication, and Experimental Validation of Novel Flexible Silicon-Based Dry Sensors for Electroencephalography Signal Measurements , 2014, IEEE Journal of Translational Engineering in Health and Medicine.

[2]  S. Leonhardt,et al.  Characterization of textile electrodes and conductors using standardized measurement setups , 2010, Physiological measurement.

[3]  Chin-Teng Lin,et al.  Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation , 2011, Sensors.

[4]  Wouter A. Serdijn,et al.  Signal Quality in Dry Electrode EEG and the Relation to Skin-electrode Contact Impedance Magnitude , 2014, BIODEVICES.

[5]  Alexander J Casson,et al.  Wearable EEG and beyond , 2019, Biomedical Engineering Letters.

[6]  J. Polich,et al.  P300 and probability: comparison of oddball and single-stimulus paradigms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  Li-Wei Ko,et al.  Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces – A Mini-Review , 2009, Gerontology.

[8]  Chin-Teng Lin,et al.  New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability , 2016, Sensors.

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

[10]  Daniel Sánchez Morillo,et al.  Dry EEG Electrodes , 2014, Sensors.

[11]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[12]  A. Baba,et al.  Measurement of the electrical properties of ungelled ECG electrodes , 2008 .

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

[14]  Chin-Teng Lin,et al.  A Novel 16-Channel Wireless System for Electroencephalography Measurements With Dry Spring-Loaded Sensors , 2014, IEEE Transactions on Instrumentation and Measurement.

[15]  Cuntai Guan,et al.  EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[17]  Yi Li,et al.  A P300 based online brain-computer interface system for virtual hand control , 2010, Journal of Zhejiang University SCIENCE C.

[18]  Jyh-Yeong Chang,et al.  Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement , 2011, IEEE Transactions on Biomedical Engineering.

[19]  Kelvin S. Oie,et al.  Cognition in action: imaging brain/body dynamics in mobile humans , 2011, Reviews in the neurosciences.

[20]  Scott E. Kerick,et al.  Brain–Computer Interface Technologies in the Coming Decades , 2012, Proceedings of the IEEE.

[21]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

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

[23]  Longchun Wang,et al.  PDMS-Based Low Cost Flexible Dry Electrode for Long-Term EEG Measurement , 2012, IEEE Sensors Journal.

[24]  Giulio Ruffini,et al.  A dry electrophysiology electrode using CNT arrays , 2005, physics/0510145.

[25]  Roberto Merletti,et al.  The electrode-skin interface and optimal detection of bioelectric signals. , 2010, Physiological measurement.

[26]  L. Kirkup,et al.  A direct comparison of wet, dry and insulating bioelectric recording electrodes. , 2000, Physiological measurement.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  Tzyy-Ping Jung,et al.  Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades , 2012, Proceedings of the IEEE.

[29]  Jyh-Yeong Chang,et al.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors , 2012, Journal of NeuroEngineering and Rehabilitation.

[30]  Scott Makeig,et al.  A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system , 2019, Journal of neural engineering.