Publikation
Comparing the Effects of Dry, Water and Gel-Based Electrodes on EEG-Based Overt Speech Classification
Tobias Jungbluth; Maurice Rekrut; Antonio Krüger
In: Proceedings of the IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering. IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE-2023), Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, October 25-27, Mailand, Italy, IEEE, 2023.
Zusammenfassung
Recent developments in EEG based overt speech recognition have shown that speech recorded with an EEG can be classified well, however there have yet to be actual applications developed for it. This is most likely due to the EEG setup being unintuitive to the layperson. The Gel-based electrodes used in most literature are both hard and time consuming to setup. To move towards a more user friendly alternative to the current standard, this work compares Dry, Water-based and Gel-based electrodes in EEG based overt speech classification. We ran a study with 20 participants collecting EEG data of speech for five keywords. Our findings show that the Temporal muscle is most important to classification, as opposed to the Frontalis and Masseter muscle for all three electrode types. However, we were also able to show that there are no overlapping important EEG channels between the three electrode types. Finally, we found that Water-based and Dry electrodes are a suitable alternative for Gel-based electrodes.