Abstract
Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten individuals without a disability) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.
Original language | English |
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Journal | I E E E Transactions on Neural Systems and Rehabilitation Engineering |
Volume | 30 |
Pages (from-to) | 678-687 |
Number of pages | 10 |
ISSN | 1534-4320 |
DOIs | |
Publication status | Published - 15 Mar 2022 |
Bibliographical note
Funding Information:This work was supported in part by the Independent Research Fund Denmark under Grant 8022-00234B and in part by VELUX FONDEN under Grant 22357.
Publisher Copyright:
© 2001-2011 IEEE.
Keywords
- Brain-computer interfaces
- EEG
- movement-related cortical potentials
- tongue