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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 …