Evaluating the impact of electrode type and montage on classification performance in Assisitve P300-based BCIs

Severe motor disabilities from neurological conditions often prevent individuals from accessing modern Assistive Technologies (AT) for communication. Brain-Computer Interfaces (BCIs) bypass motor pathways, translating brain activity into device control. Among non-invasive BCIs, P300-based systems, exploiting the P300 Event-Related Potential, are widely investigated for communication: in the P300-speller users select letters by focusing on target stimuli, enabling communication for individuals with severe motor impairments. BCIs are therefore promising, but usability, setup complexity, and gel-based EEG caps limit integration. Traditional gel-based caps provide high-quality signals but are uncomfortable and slow to set up, while semi-dry headsets improve comfort and usability, but typically have suboptimal montages for P300-applications. This raises questions about the effects of electrode type and montage. This study compared a commercial semi-dry 7-channel headset for out-of-the-lab applications and a traditional wet EEG cap during P300-speller tasks. Classification performance was evaluated through online and offline analyses, providing insight into how electrode design and configuration influence the usability and effectiveness of BCIs.