MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI...

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Published in:IEEE transactions on biomedical engineering Vol. 69; no. 6; pp. 2105 - 2118
Main Authors: Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
autoencoder (AE)
Brain-computer interfaces (BCIs)
Calibration
Classification
deep metric learning (DML)
EEG
Electroencephalography
Feature extraction
Human-computer interface
Image classification
Interfaces
Learning
Measurement
Mental task performance
motor imagery (MI)
Motor skill learning
multi-task learning
Multitasking
Representations
Task analysis
Training
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Summary:Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. Methods: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. Results: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. Conclusion: We demonstrate that MIN2Net improves discriminative information in the latent representation. Significance: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2021.3137184