Abstract
As a step toward developing neuroprostheses, the purpose of this study is to explore syllable decoding in a subject with implanted electrocorticographic (ECoG) recordings. For this study, we use ECoG signals recorded while a subject volunteered to perform a task in which the patient has been visually cued to speak isolated consonant-vowel syllables varying in their articulatory features. We propose a recursive estimation method to calculate the parametric model coefficients in each time instant and band power features from individual ECoG sites are extracted to decode the articulated syllables. Our findings may contribute to the development of brain machine interface (BMI) systems for syllable-level speech rehabilitation in handicapped individuals.
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Kanas, V.G., Mporas, I., Milsap, G.W., Sgarbas, K.N., Crone, N.E., Bezerianos, A. (2015). Time-Varying Parametric Modeling of ECoG for Syllable Decoding. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_22
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DOI: https://doi.org/10.1007/978-3-319-23344-4_22
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