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Pre-Decision Feedback in Code-Modulated Visual Evoked Potentials Brain-Computer Interface for an 11-class Keypad Typing Task
Conference paper   Open access

Pre-Decision Feedback in Code-Modulated Visual Evoked Potentials Brain-Computer Interface for an 11-class Keypad Typing Task

J. Gomel, J. J. Torre Tresols, P. Cimarosto, K. Cabrera Castillos and F. Dehais
2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 3800-3805
05 Oct 2025
url
https://hal.science/hal-0532600View

Abstract

Accuracy Brain-computer interfaces Calibration Decoding Performance metrics Stars Training User experience Visualization Electroencephalography
This paper proposes to investigate the design of user-friendly reactive Brain-Computer Interfaces (rBCIs) based on Code-Modulated Visual Evoked Potentials (c-VEP). The BCI was implemented using the StAR-Burst paradigm, which features small, randomly-oriented texture patches designed to optimize foveal neural responses while enhancing user visual comfort. We extended this paradigm to support an 11-class selection scenario using dry EEG electrodes. In addition, the study explored the impact of predictive visual feedback on user experience. Two feedback types-Halo and Depth-were compared against a control condition with no feedback, aiming to enhance users' sense of control during interaction. Results demonstrated that the BCI achieved high classification accuracy with a dry EEG system across all three conditions (mean = 93,3%), using only 33 seconds of calibration data. Contrary to our expectations, predictive feedback did not lead to significant improvements in classification accuracy or decoding time. However, the Halo feedback significantly increased users' anticipation of success, though it also caused greater peripheral distraction. Interestingly, decoding time improved significantly with practice, underscoring the role of user adaptation in enhancing performance. Overall, the findings highlight the need for explicit training to help users effectively interpret and utilize predictive feedback.

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