In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, there still exist BCI-illiterate subjects who struggle to use BCI systems, showing high intra-subject variabilities. Several methods have been proposed to enhance their performance via transfer learning. In such a case, high inter- and intrasubject variabilities are the points to be considered. Transfer learning , especially as a domain adaptation technique, has drawn increasing attention in various fields. However, the adaptation of approaches into BCI faces two challenging limitations. (i) Most domain adaptation  methods are designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. (ii) Most of the existing methods do not consider a negative transfer to disrupt generalization ability. In this paper, we propose a novel network architecture to tackle these limitations by estimating mutual information in high- and low-level representationsregardless of the domain that is considered as a subject in this paper. Specifically, our proposed method extracts subject-invariant and class-relevant features, thereby enhancing generalizability in overall classification. It is also noteworthy that our method can be applicable to a new subject with a small amount of data via fine-tuning, thus reducing calibration time for its practical uses. We validated our proposed method on two large motor imagery EEG datasets via comparisons with other competing methods .
In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no concern of its practical use, hampered by time-consuming steps and a large data requirement. To this end, recent studies adopted a transfer learning strategy, especially domain adaptation techniques. Among those, to our knowledge, an adversarial learning has shown its potential in BCIs. In the meantime, it is known that adversarial learning-based domain adaptation methods are prone to negative transfer that disrupts learning generalized feature representations, applicable to diverse domains, e.g., subjects or sessions in BCIs. In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning. To be specific, we devise two operational components in a deep network that explicitly estimate mutual information between feature representations; (1) to decompose features in an intermediate layer into class-relevant and class-irrelevant ones, (2) to enrich class-discriminative feature representation. On two large EEG datasets, we validated the effectiveness of our proposed framework by comparing with several comparative methods in performance. Further, we conducted rigorous analyses by performing an ablation study in regard to the components in our network, explaining our model's decision on input EEG signals via layer-wise relevance propagation, and visualizing the distribution of learned features via t-SNE .
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there still exist BCI-illiterate subjects who struggle to use BCI systems, showing high intra-subject variabilities. Several methods have been proposed to enhance their performance via transfer learning. In such a case,
owing to
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inter- and intrasubject variabilities are the points to be considered. Transfer learning , especially as a domain adaptation technique, has drawn increasing attention in various fields. However, the adaptation of approaches into BCI faces two challenging limitations. (i) Most
intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no concern of its practical use, hampered by time-consuming steps and a large data requirement. To this end, recent studies adopted a transfer learning strategy, especially
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techniques. Among those, to our knowledge, an adversarial learning has shown its potential in BCIs. In the meantime, it is known that adversarial learning-based domain adaptation
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designed for labeled source and unlabeled target domain whereas BCI tasks generally have multiple annotated domains. (ii) Most of the existing methods do not consider a negative transfer to disrupt generalization ability.
prone to negative transfer that disrupts learning generalized feature representations, applicable to diverse domains, e.g., subjects or sessions in BCIs.
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network architecture to tackle these limitations by estimating mutual information in high-
framework that learns class-relevant
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low-level representationsregardless of the domain that is considered as a subject in this paper. Specifically, our proposed method extracts
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feature representations in an information-theoretic manner, without using adversarial learning. To be specific, we devise two operational components in a deep network that explicitly estimate mutual information between feature representations; (1) to decompose features in an intermediate layer into
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features, thereby enhancing generalizability in overall classification. It is also noteworthy that our method can be applicable to a new subject with a small amount of data via fine-tuning, thus reducing calibration time for its practical uses. We validated our proposed method on two large motor imagery EEG datasets via comparisons with other competing methods
and class-irrelevant ones, (2) to enrich class-discriminative feature representation. On two large EEG datasets, we validated the effectiveness of our proposed framework by comparing with several comparative methods in performance. Further, we conducted rigorous analyses by performing an ablation study in regard to the components in our network, explaining our model's decision on input EEG signals via layer-wise relevance propagation, and visualizing the distribution of learned features via t-SNE