Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain

Kim, Hyunji; Wang, In-Nea; Kim, Young‐Tak; Kim, Hakseung; Kim, Dong-Joo

doi:10.1109/bci48061.2020.9061636

Cited by 6 publications

(1 citation statement)

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“…The major contributions of this paper are summarized as follows. [26][27][28][29]. Among them, common spatial pattern (CSP) [15,16] is one of the most popular algorithm to detect discriminative movementrelated patterns for motor imagery tasks.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Joint Domain Adaptation Network for Motor Imagery Classification

Hong

Zheng

Liu

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to its convenience and reliability. The EEG-based BCI applications are majorly limited by the time-consuming calibration procedure for discriminative feature representation and classification. Existing EEG classification methods either heavily depend on the handcrafted features or require adequate annotated samples at each session for calibration. To address these issues, we propose a novel dynamic joint domain adaptation network based on adversarial learning strategy to learn domain-invariant feature representation, and thus improve EEG classification performance in the target domain by leveraging useful information from the source session. Specifically, we explore the global discriminator to align the marginal distribution across domains, and the local discriminator to reduce the conditional distribution discrepancy between subdomains via conditioning on deep representation as well as the predicted labels from the classifier. In addition, we further investigate a dynamic adversarial factor to adaptively estimate the relative importance of alignment between the marginal and conditional distributions. To evaluate the efficacy of our method, extensive experiments are conducted on two public EEG datasets, namely, Datasets IIa and IIb of BCI Competition IV. The experimental results demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.Index Terms-Deep neural network (DNN), domain adaptation, adversarial learning, electroencephalogram (EEG), motor imagery (MI), brain-computer interface (BCI).

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