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Decoding Imagined Speech Based on Deep Metric Learning for Intuitive BCI Communication

Authors
Lee, Dong-YeonLee, MinjiLee, Seong-Whan
Issue Date
2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Electroencephalography; Entropy; Feature extraction; Imagined speech; Neural networks; Scalability; Speech recognition; Training; brain-computer interface; deep metric learning; instantaneous frequency; spectral entropy
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.29, pp.1363 - 1374
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume
29
Start Page
1363
End Page
1374
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138494
DOI
10.1109/TNSRE.2021.3096874
ISSN
1534-4320
Abstract
Imagined speech is a highly promising paradigm due to its intuitive application and multiclass scalability in the field of brain-computer interfaces. However, optimal feature extraction and classifiers have not yet been established. Furthermore, retraining still requires a large number of trials when new classes are added. The aim of this study is (i) to increase the classification performance for imagined speech and (ii) to apply a new class using a pretrained classifier with a small number of trials. We propose a novel framework based on deep metric learning that learns the distance by comparing the similarity between samples. We also applied the instantaneous frequency and spectral entropy used for speech signals to electroencephalography signals during imagined speech. The method was evaluated on two public datasets (6-class Coretto DB and 5-class BCI Competition DB). We achieved a 6-class accuracy of 45.00 +/- 3.13% and a 5-class accuracy of 48.10 +/- 3.68% using the proposed method, which significantly outperformed state-of-the-art methods. Additionally, we verified that the new class could be detected through incremental learning with a small number of trials. As a result, the average accuracy is 44.50 +/- 0.26% for Coretto DB and 47.12 +/- 0.27% for BCI Competition DB, which shows similar accuracy to baseline accuracy without incremental learning. Our results have shown that the accuracy can be greatly improved even with a small number of trials by selecting appropriate features from imagined speech. The proposed framework could be directly used to help construct an extensible intuitive communication system based on brain-computer interfaces.
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