Adjusting Classical BCI Paradigms Parameters to Optimize Performance with a Wearable Dry-Electrode EEG Device: Towards Operational Conditions

This study explores the optimization of classical Brain-Computer Interface (BCI) paradigms—Motor Imagery (MI) and Steady-State Visual Evoked Potentials (SSVEP)—using a portable, dry-electrode EEG system in operating conditions. By adjusting key parameters, such as stimulation frequency, window length, and classifier type, we aim to bridge the gap between controlled laboratory settings and practical BCI applications. Experiments were conducted across varied environments and times to capture ecological variability. Our results reveal strong inter- and intra-subject performance variability, emphasizing the need for personalized adaptation. In MI, we evaluated three classification methods, with TSLDA offering the best trade-off for real-time use. For SSVEP, we assessed target properties and high-frequency stimuli to enhance comfort without sacrificing signal quality. The dry EEG device demonstrated performance comparable to wet systems, with certain participants achieving high classification accuracy and Information Transfer Rates in real-time conditions. These findings highlight the feasibility of deploying dry EEG-based BCIs outside laboratory settings, provided careful tuning of paradigm-specific parameters. This work lays a foundation for developing adaptive BCI systems designed for operational deployment.