Abstract
Code-modulated visual evoked potentials (c-VEPs) have potential as a reliable and non-invasive control signal for brain-computer interfaces (BCIs). However, these systems need to become more user-friendly. Non-binary codes have been proposed to reduce visual fatigue, but there is still a lack of adaptive methods to shorten trial durations. To address this, we propose a nonparametric early stopping algorithm for the non-binary circular shifting paradigm. The algorithm analyzes the distribution of unattended commands’ correlations and stops stimulation when the most probable correlation is considered an outlier. This proposal was evaluated offline with 15 healthy participants using p-ary maximal length sequences encoded with shades of gray. Results showed that the algorithm could stop stimulation in under two seconds for all sequences, achieving mean accuracies over 95%. The highest performances were achieved by bases \(p=2\) and \(p=5\), attaining 98.3% accuracy with ITRs of 164.8 bpm and 121.7 bpm, respectively. The proposed algorithm reduces required cycles without compromising accuracy for c-VEP-based BCI systems.
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Acknowledgements
This research was supported by projects TED2021-12991 5B-I00, RTC2019-007350-1 and PID2020-115468RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and ‘European Union NextGenerationEU/PRTR’; and by ‘Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)’ through ‘Instituto de Salud Carlos III’ co-funded with European Regional Development Fund (ERDF) funds. E. Santamaría-Vázquez was in receipt of a PIF grant by the ‘Consejería de Educación de la Junta de Castilla y León’.
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Martínez-Cagigal, V., Santamaría-Vázquez, E., Hornero, R. (2023). Toward Early Stopping Detection for Non-binary c-VEP-Based BCIs: A Pilot Study. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_47
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