Auto-calibration of c-VEP BCI by word prediction

Federica Turi (1, 2) , Nathalie T.H. Gayraud (1, 2) , Maureen Clerc (1, 2)

1 ATHENA - Computational Imaging of the Central Nervous System
2 UniCA - Université Côte d'Azur

A code-modulated Visual Evoked Potential Brain Computer Interface (c-VEP BCI) allows for spelling from a virtual keyboard of flashing characters. All characters flash simultaneously, and each character flashes according to a predefined pseudo-random binary sequence, circular-shifted by a different time lag. For a given character, the pseudo-random stimulus sequence evokes a VEP in the electroencephalogram (EEG) of the subject, which can be used as a template. This template is usually obtained during a calibration phase and it is applied for the target identification during the spelling phase. A downside of a c-VEP BCI system is that it needs a long calibration phase to reach good performance. This paper proposes an unsupervised method that avoids the calibration phase in a c-VEP BCI, by extracting relative lags from the VEP responses, between successive characters, and predicting the full word using a dictionary. We tested it in offline experiments on a public dataset. We simulated the spelling of four groups of words with a different total number of characters selected from an English dictionary. Each experiment is parameterized by the number of stimulus cycles. The obtained results show that a word-prediction-based auto-calibration method in c-VEP BCIs can be efficient and effective.

Mots clés: c-VEP BCI; Brain Computer Interface; auto-calibration; word prediction

Domaines: Sciences cognitives; Neurosciences; Interface homme-machine [cs.HC]; Traitement du signal et de l'image [eess.SP]; Ingénierie biomédicale; Apprentissage [cs.LG]; Systèmes et contrôle [cs.SY]

Federica Turi, Nathalie T.H. Gayraud, Maureen Clerc. Auto-calibration of c-VEP BCI by word prediction. 2020. ⟨hal-02844024⟩