Single-Source and Multi-Source Cross-Subject Transfer Based on Domain Adaptation Algorithms for EEG Classification

Transfer learning (TL) has been employed in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) to enhance performance for cross-session and cross-subject EEG classification. However, domain shifts coupled with a low signal-to-noise ratio between EEG recordings have been demonstrated to contribute to significant variations in EEG neural dynamics from session to session and subject to subject. Critical factors-such as mental fatigue, concentration, and physiological and non-physiological artifacts-can constitute the immense domain shifts seen between EEG recordings, leading to massive inter-subject variations. Consequently, such variations increase the distribution shifts across the source and target domains, in turn weakening the discriminative knowledge of classes and resulting in poor cross-subject transfer performance. In this paper, domain adaptation algorithms, including two machine learning (ML) algorithms, are contrasted based on the single-source-to-single-target (STS) and multi-source-to-single-target (MTS) transfer paradigms, mainly to mitigate the challenge of immense inter-subject variations in EEG neural dynamics that lead to poor classification performance. Afterward, we evaluate the effect of the STS and MTS transfer paradigms on cross-subject transfer performance utilizing three EEG datasets. In this case, to evaluate the effect of STS and MTS transfer schemes on classification performance, domain adaptation algorithms (DAA)-including ML algorithms implemented through a traditional BCI-are compared, namely, manifold embedded knowledge transfer (MEKT), multi-source manifold feature transfer learning (MMFT), k-nearest neighbor (K-NN), and Na & iuml;ve Bayes (NB). The experimental results illustrated that compared to traditional ML methods, DAA can significantly reduce immense variations in EEG characteristics, in turn resulting in superior cross-subject transfer performance. Notably, superior classification accuracies (CAs) were noted when MMFT was applied, with mean CAs of 89% and 83% recorded, while MEKT recorded mean CAs of 87% and 76% under the STS and MTS transfer paradigms, respectively.