Abstract
Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.
Highlights
IntroductionBrain–computer interfaces (BCIs) (Dornhege et al, 2007; Lotte et al, 2018; Roy et al, 2019) provide communication pathways between a user and an external device (e.g., robotic arm, speller, seizure alarm system, etc.) by measuring and analyzing brain signals
Brain–computer interfaces (BCIs) (Dornhege et al, 2007; Lotte et al, 2018; Roy et al, 2019) provide communication pathways between a user and an external device by measuring and analyzing brain signals
Implicit Transfer Learning Methods we describe the implicit TL approaches in DL-based BCIs
Summary
Brain–computer interfaces (BCIs) (Dornhege et al, 2007; Lotte et al, 2018; Roy et al, 2019) provide communication pathways between a user and an external device (e.g., robotic arm, speller, seizure alarm system, etc.) by measuring and analyzing brain signals. Non-invasive BCIs based on electroencephalography (EEG) are commonly exploited (Suk and Lee, 2012; Roy et al, 2019). The real-world impact of BCIs is promising because they can identify intention-reflected brain activities. An active BCI (Fahimi et al, 2020) recognizes complex patterns from EEG spontaneously caused by a user’s intention independent of external stimuli, and a reactive BCI (Won et al, 2019) identifies brain activities in reaction to external events. A Passive BCI (Ko et al, 2020b) is exploited to acquire implicit information of a user’s cognitive status without any voluntary control
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