Abstract
In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.
Highlights
A brain–computer interface (BCI) can allow a subject to directly control an external electronic device using his brain signals without the participation of his peripheral nerves and muscles (Vidal, 1977; Kübler et al, 2001; Wolpaw et al, 2002)
Based on many papers surveyed we briefly summarize various signal processing approaches to reduce calibration effort in EEG-based BCI
(1) In terms of EEG-based BCI Compared with the traditional EEGs, such as state visual evoked potentials (SSVEP) and P300, Motor imagery (MI) EEG is more widely studied in the small training set scenario
Summary
A brain–computer interface (BCI) can allow a subject to directly control an external electronic device using his brain signals without the participation of his peripheral nerves and muscles (Vidal, 1977; Kübler et al, 2001; Wolpaw et al, 2002). Users may suffer from visual fatigue after long-term gazing at the flickering stimulus Besides these traditional paradigms, some meaningful paradigms become emerging in EEG-based BCI. In the calibration phase, labeled samples from each subject are successively processed by different modules to train a subject-specific classifier. The raw EEG signals are invoked by assigned BCI tasks. The raw EEG signals are invoked by unknown human intentions and identified in the classification module. A set of labeled features are input to the classification module to build a subject-specific classifier. The neurophysiological processes often vary over time and across subjects in most EEG-based BCIs. For each subject, long calibration procedure is always needed to collect amounts of labeled EEG signals to build a steady recognition model.
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