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Abstract:
Robust dry EEG electrodes are arguably the key to making EEG Brain-Computer Interfaces
(BCIs) a practical technology. Existing studies on dry EEG electrodes can be characterized
by the recording method (stand-alone dry electrodes or simultaneous recording with wet
electrodes), the dry electrode technology (e.g. active or passive), the paradigm used for testing
(e.g. event-related potentials), and the measure of performance (e.g. comparing dry and wet
electrode frequency spectra). In this study, an active-dry electrode prototype is tested, during
a motor-imagery task, with EEG-BCI in mind. It is used simultaneously with passive-wet
electrodes and assessed using offline classification accuracy. Our results indicate that the two
types of electrodes are comparable in their performance but there are improvements to be
made, particularly in finding ways to reduce motion-related artifacts.
