Abstract
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.
Highlights
The brain-computer interface (BCI) offers a new pathway of communication between an external device and the brain through transforming metabolic or electrophysiological brain activities to control messages for devices and applications
We proposed the transfer kernel CSP (TKCSP) method to lower the training trial amount and improve the performance via learning a domain-independent kernel
TKCSP and six competitive approaches were evaluated on EEG datasets provided by BCI Competition III
Summary
The brain-computer interface (BCI) offers a new pathway of communication between an external device and the brain through transforming metabolic or electrophysiological brain activities to control messages for devices and applications. The electroencephalogram (EEG) obtains time series data with multiple variants recorded at several sensors pressed on the scalp. It thereby presents electrical potentials under the induction of brain activities. Classification performance promotion of BCI systems based on the EEG has significant challenges It is necessary for a fresh subject to conduct a lengthy calibration session for sufficient training sample collection to establish classifiers and extractors of subject-specific features. Suitable methods must be identified to strengthen the performance This is because a short calibration session means the availability of merely a few training samples for target users, which may result in overfitting or suboptimal feature classifiers or extractors
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