In recent  , deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, due 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 much concern on its less practical, time-consuming, and data-hungry process. To tackle this problem, recent studies took advantage of transfer learning, especially using domain adaptation techniques . However, there still remain 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 methods do not consider negatively transferable to disrupt generalization ability. In this paper, we propose a novel network architecture to tackle those limitations by estimating mutual information in high-level representation and low-level representation, separately . Specifically, our proposed method extracts domain-invariant and class-relevant features, thereby enhancing generalizability in classification across . It is also noteworthy that our method can be applicable to a new subject with a small amount of data via a fine-tuning, step only, reducing calibration time for  practical uses. We validated our proposed method on a big motor imagery EEG dataset by showing promising results, compared to competing methodsconsidered in our experiments .
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 representations regardless 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 .
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years
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due to
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,
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intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no much concern on its less practical, time-consuming, and data-hungry process. To tackle this problem, recent studies took advantage of transfer
inter- and intrasubject variabilities are the points to be considered. Transfer
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using domain adaptation techniques
as a domain adaptation technique, has drawn increasing attention in various fields
R
there still remain
the adaptation of approaches into BCI faces
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; i) most
. (i) Most
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ii) most of the
(ii) Most of the existing
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negatively transferable
a negative transfer
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those
these
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high-level representation
high-
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representation, separately
representations regardless of the domain that is considered as a subject in this paper
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domain-invariant
subject-invariant
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classification across
overall classification
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a
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step only,
thus
A
its
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a big
two large
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dataset by showing promising results, compared to competing methodsconsidered in our experiments
datasets via comparisons with other competing methods