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A Prototypical Network for Few-shot Recognition of Speech Imagery Data by Alan Hernandez-Galvan; Graciela Ramirez-Alonso; Juan Ramirez-Quintana is a Medicine article available to read on EtoBox.

What is A Prototypical Network for Few-shot Recognition of Speech Imagery Data about?

Speech imagery (SI) is a Brain-Computer Interface (BCI) paradigm based on EEG signals analysis where the user imagines speaking out a vowel, phoneme, syllable, or word without producing any sound or facial movements. This paradigm is ideal for developing interfaces for patients diagnosed with neurological disorders since it helps them communicate with their surroundings. This paper presents a prototypical network named Proto-Speech to classify vowels, syllables, and words acquired with the SI paradigm. The embeddings of the prototypical network are produced with a 1D convolutional layer and two bidirectional Gated Recurrent Unit (GRU) layers. The meta-training strategy of Proto-Speech considers the eleven classes of the KaraOne dataset, and the meta-testing is configured with five binary classification tasks commonly used in KaraOne, and with an extra multi-classification scheme. Also, a second publically available dataset named ASU is used in meta-testing to classify long words, short words, vowels, short-long words, and a multiclass approach. Both meta-training and meta-testing are implemented in a subject-independent strategy. Experimental results indicate the best average accur

Who reads A Prototypical Network for Few-shot Recognition of Speech Imagery Data?

Author

Alan Hernandez-Galvan; Graciela Ramirez-Alonso; Juan Ramirez-Quintana

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)