Year: 2024
Type: conference-paper
Abstract: Dry electroencephalography (EEG) electrodes have emerged as a promising solution for enhancing the usability of non-invasive Brain-Computer Interface (BCI) systems. Recent advancements in dry helmets have demonstrated competitiveness in enabling EEG and BCI applications with state-of-the-art gel-based systems. In this study, we evaluate the performance and signal quality of a dry EEG cap for BCI applications in a large population. The assessment was conducted under extremely noisy conditions at a public exhibition, surrounded by numerous attendees and multiple sources of electromagnetic interference. Our analysis includes 100 participants from the Mental Work exhibition and assesses the acquired signal's integrity through, Motor Imagery (MI) classification accuracy, the kurtosis of the raw signals and the deviation of the Power Spectral Density (PSD) spectra from the ideal curve. Results show that 56 participants achieved above chance-level single-sample accuracy, with even higher accuracy observed for peak performance. Frontal channels exhibited higher artifact presence, yet the overall signal quality was comparable to gel-based systems used in the authors' previous studies. These findings confirm the potential of dry EEG technology to enable BCI applications beyond the lab, facilitating everyday use in homes, clinics, and public spaces.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4405755375