PhD Scientific Days 2024
Budapest, 9-10 July 2024
Neurosciences
Validation of portable, dry electrode-based electroencephalography device for application in brain–computer interface solutions
Előadó neve
Rácz, Melinda, MSc
Neptun code
FIGCJ1
Előadó munkahelye
Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences
Előadó telefonszáma
+36707046849
Előadó e-mail címe
racz.melinda.9157@gmail.com
Az előadás címe
Validation of portable, dry electrode-based electroencephalography device for application in brain–computer interface solutions
Szerző(k) neve és munkahelye
Melinda Rácz1,2,3, János Csipor4, István Ulbert2, Gergely Márton2
1: School of PhD Studies, Semmelweis University, Budapest, Hungary
2: Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
3: Selye János Doctoral College for Advanced Studies, Semmelweis University, Budapest, Hungary
4: MindRove Kft., Győr, Hungary
Bemutatás módja
Szóbeli
Szekció
Neurosciences
Language of the presentation
English
Preferred session
Neurosciences
Összefoglaló szövege
Aims: nowadays, commercial lightweight electroencephalography (EEG) headsets such as Emotiv EPOC and Muse are gaining popularity in the neuroscience community, mostly in research that monitors natural behaviour outside traditional laboratory settings. These devices often utilize only a few dry or active electrodes in specific locations and signal quality is often inferior compared to their traditional counterparts equipped with electrode caps that require conductive paste for proper functioning. In this study, we wanted to assess the feasibility of a portable, dry electrode-based EEG headset manufactured by MindRove for laboratory use.
Methods: we implemented three paradigms for acquiring visual evoked potential (VEP), P300 event related potential and motor imagery (MI) related cortical patterns. Measurements were taken using the MindRove device, with a wet electrode system (mBrainTrain Smarting) applied as reference. The performance of the two devices were assessed using traditional signal quality measures (e.g. signal-to-noise ratio) for VEP and P300. Since motor imagery patterns are not trivial to quantify and MI is often utilized in brain–computer interfaces (BCIs) that employ machine learning algorithms, neural network-based and support vector machine-based classifiers were fit to the two MI databases to benchmark the usability of the MindRove headset for use in BCI solutions.
Results: based on preliminary research involving eight subjects, signals recorded using the two systems were comparable, with the wet electrode device performing only slightly better than the more easily applicable, easy-to-use commercial headset.
Conclusions: the application of the portable MindRove device is feasible for use in research besides qualitative investigations.
Funding: Melinda Rácz is thankful for the SE 250 + Doctoral Scholarship for Excellence (supported by project EFOP-3.6.3-VEKOP-16-2017-00009 ‘Az orvos-, egészségtudományi-és gyógyszerészképzés tudományos műhelyeinek fejlesztése’). Melinda Rácz was also supported by the ÚNKP-23-3-II-SE-84 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.
University
Semmelweis University
Supervisor
Dr. Gergely Márton
Publication of my abstract
I give consent to the publication of my abstract on the website of the congress.
Kind
Szabad
Status
elfogadva
Accepted presentation method
szóbeli
Előadás fájl jóváhagyás
nem rendelkezett róla
Előadó
6824
Start
11:00
End
11:10