Abstract
One important aspect in non-invasive brain–computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.
Highlights
Measuring electrical activity of the human brain and utilizing this data to bypass the traditional motor output pathways of the nervous system is one of the main purposes of brain– computer interface (BCI) systems
Two main factors that impede the widespread use of BCIs for healthy as well as for severely impaired people are the BCI control method and the EEG signal acquisition system to measure the signals
We consider three control methods based on different brain signals: (i) neural oscillations, (ii) event-related potentials (ERP), and (iii) steady-state evoked potentials (SSEP)
Summary
Measuring electrical activity of the human brain and utilizing this data to bypass the traditional motor output pathways of the nervous system is one of the main purposes of brain– computer interface (BCI) systems. We consider three control methods based on different brain signals: (i) neural oscillations, (ii) event-related potentials (ERP), and (iii) steady-state evoked potentials (SSEP). A typical BCI based on neural oscillations, for example, utilizes the fact that defined frequency components of the EEG signal create a typical pattern briefly before, during, and after movement execution and less pronounced at movement imagination (e.g., Pfurtscheller et al, 2000; Faller et al, 2014; Schwarz et al, 2015).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
