{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:02:13Z","timestamp":1781305333577,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T00:00:00Z","timestamp":1682985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"A*STAR","award":["152 148 0028"],"award-info":[{"award-number":["152 148 0028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (&lt;150 k\u2126) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver\u2013silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10\u201320 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 k\u2126. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.<\/jats:p>","DOI":"10.3390\/s23094453","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T01:36:38Z","timestamp":1683077798000},"page":"4453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3119-9545","authenticated-orcid":false,"given":"Ramona B.","family":"Damalerio","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Lim","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan-Tan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6322-1255","authenticated-orcid":false,"given":"Ming-Yuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Agency for Science, Technology and Research, Singapore 138634, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sharma, M., Tiwari, J., and Acharya, U. (2021). Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18063087"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.cnp.2020.12.002","article-title":"Under-sampling in epilepsy: Limitations of conventional EEG","volume":"6","author":"Baud","year":"2021","journal-title":"Clin. Neurophysiol. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"78060","DOI":"10.1109\/ACCESS.2021.3083519","article-title":"Impact of EEG Parameters Detecting Dementia Diseases: A Systematic Review","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103040","DOI":"10.1016\/j.nicl.2022.103040","article-title":"Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review","volume":"35","author":"Doval","year":"2022","journal-title":"NeuroImage Clin."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.clinph.2020.10.024","article-title":"EEG functional connectivity contributes to outcome prediction of postanoxic coma","volume":"132","author":"Keijzer","year":"2021","journal-title":"Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions recognition using EEG signals: A survey","volume":"10","author":"Alarcao","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.1007\/s11042-022-12310-7","article-title":"CNN and LSTM based ensemble learning for humanemotion recognition using EEG recording","volume":"82","author":"Iyer","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1109\/TAFFC.2022.3210958","article-title":"Exploring Self-Attention Graph Pooling With EEG-Based Topological Structure and Soft Label for Depression Detection","volume":"13","author":"Chen","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102936","DOI":"10.1016\/j.bspc.2021.102936","article-title":"Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals","volume":"70","author":"Baygin","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_10","unstructured":"Ang, K., Chua, K., Guan, C., Ang, B., Kuah, C., Wang, C., Phua, K., Chin, Z., and Zhang, H. (September, January 31). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cincotti, F., Pichiorri, F., Arico, P., Aloise, F., Leotta, F., de Vico Fallani, F., del R. Mill\u00e1n, J., Molinari, M., and Mattia, D. (September, January 28). EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346871"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s10548-022-00915-y","article-title":"Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review","volume":"35","author":"Milani","year":"2022","journal-title":"Brain Topogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/TBME.2019.2921198","article-title":"Assessment of the Efficacy of EEG-Based MI-BCI with Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation","volume":"67","author":"Foong","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.7717\/peerj.38","article-title":"Validation of the Emotiv EPOC\u00ae EEG gaming system for measuring research quality auditory ERPs","volume":"1","author":"Badcock","year":"2013","journal-title":"PeerJ"},{"key":"ref_15","unstructured":"Shin, J., Islam, M., and Molla, M. (2022, January 10\u201312). Natural Human Emotion Recognition Based on Various Mixed Reality(MR) Games and Electroencephalography (EEG) Signals. Proceedings of the 5th IEEE Eurasian Conference on Educational Innovation 2022, Taipei, Taiwan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100635","DOI":"10.1016\/j.dcn.2019.100635","article-title":"Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges","volume":"36","author":"Lau","year":"2019","journal-title":"Dev. Cogn. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fnins.2011.00053","article-title":"A dry EEG-system for scientific research and brain-computer interfaces","volume":"5","author":"Zander","year":"2011","journal-title":"Front. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"O\u2019Sullivan, M., Temko, A., Bocchino, A., O\u2019Mahony, C., Boylan, G., and Popovici, E. (2019). Analysis of a Low-Cost EEG Monitoring System and Dry Electrodes toward Clinical Use in the Neonatal ICU. Sensors, 19.","DOI":"10.3390\/s19112637"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Castiblanco Jimenez, I.A., Gomez Acevedo, J.S., Olivetti, E.C., Marcolin, F., Ulrich, L., Moos, S., and Vezzetti, E. (2022). User Engagement Comparison between Advergames and Traditional Advertising Using EEG: Does the User\u2019s Engagement Influence Purchase Intention?. Electronics, 12.","DOI":"10.3390\/electronics12010122"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1097\/WNP.0000000000000316","article-title":"American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature","volume":"33","author":"Acharya","year":"2016","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s12938-018-0617-y","article-title":"Optimal combination of electrodes and conductive gels for brain electrical impedance tomography","volume":"17","author":"Yang","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nunes, T., and da Silva, H. (2023). Characterization and Validation of Flexible Dry Electrodes for Wearable Integration. Sensors, 23.","DOI":"10.3390\/s23031468"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ng, C.R., Fiedler, P., Kuhlmann, L., Liley, D., Vasconcelos, B., Fonseca, C., Tamburro, G., Comani, S., Lui, T.K.-Y., and Tse, C.-Y. (2022). Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps. Sensors, 22.","DOI":"10.3390\/s22208079"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14565","DOI":"10.1109\/JSEN.2020.3012394","article-title":"Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review","volume":"20","author":"Shad","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12847","DOI":"10.3390\/s140712847","article-title":"Dry EEG Electrodes","volume":"14","author":"Valle","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8381","DOI":"10.1111\/ejn.15037","article-title":"Does the electrode amplification style matter? A comparison of active and passive EEG system configurations during standing and walking","volume":"54","author":"Debener","year":"2021","journal-title":"Eur. J. Neurosci."},{"key":"ref_27","unstructured":"Connor, R. (2022). Dry EEG Electrode for Use on a Hair-Covered Portion of a Person\u2019s Head. (US 2022\/0233124 A1), U.S. Patent."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"541052","DOI":"10.3389\/fnhum.2020.541052","article-title":"Effects of Transcranial Direct Current Stimulation on Brain Networks Related to Creative Thinking","volume":"14","author":"Koizumi","year":"2020","journal-title":"Front. Hum. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e1054","DOI":"10.1097\/PR9.0000000000001054","article-title":"Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain","volume":"7","author":"Ocay","year":"2022","journal-title":"Pain Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Katona, J., Farkas, I., Ujbanyi, T., Dukan, P., and Kovari, A. (2014, January 23\u201325). Evaluation Of The Neurosky MindFlex EEG Headset Brain Waves Data. Proceedings of the 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl\u2019any, Slovakia.","DOI":"10.1109\/SAMI.2014.6822382"},{"key":"ref_31","unstructured":"Chi, Y.M., Elconin, M.H., and Kerth, T.A. (2013). Transducer Assemblies for Dry Applications of Transducers. (WO2013\/142316 A1), WIPO Patent."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Damalerio, R., and Cheng, M.-Y. (2020, January 3\u201330). Development of Dry EEG Electrodes and Dry EEG Cap for Neuromonitoring. Proceedings of the IEEE 70th Electronic Components and Technology Conference (ECTC), Orlando, FL, USA.","DOI":"10.1109\/ECTC32862.2020.00137"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.clinph.2004.10.001","article-title":"Evaluation of commercially available electrodes and gels for recording of slow EEG potentials","volume":"116","author":"Tallgren","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"ref_34","unstructured":"Dow (2017). SYLGARD\u2122 160 Silicone Elastomer Kit Technical Data Sheet, The Dow Chemical Company."},{"key":"ref_35","unstructured":"Dow (2017). SYLGARD\u2122 184 Silicone Elastomer Kit Technical Data Sheet, The Dow Chemical Company."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e3019","DOI":"10.1002\/cem.3019","article-title":"ANOVA tables and statistical significance of models","volume":"33","author":"Brereton","year":"2019","journal-title":"J. Chemom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41747-020-0145-y","article-title":"Statistical significance: P value, 0.05 threshold, and applications to radiomics\u2014Reasons for a conservative approach","volume":"4","author":"Sardanelli","year":"2020","journal-title":"Eur. Radiol. Exp."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abhang, P., Gawali, B., and Mehrota, S. (2016). Introduction to EEG- and Speech-Based Emotion Recognition, Academic Press.","DOI":"10.1016\/B978-0-12-804490-2.00007-5"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4453\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:28:30Z","timestamp":1760124510000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,2]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094453"],"URL":"https:\/\/doi.org\/10.3390\/s23094453","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,2]]}}}