Datensatz

Zusammenfassung

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.