Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

Sun

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the timeconsuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter-and intrasubject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the interand intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. Methods: IISMC extracts subjectspecific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter-and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. Results: The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). Significance: The proposed method has great potential for developing highspeed BCIs.

Section: Discussionmentioning

confidence: 99%

Cross-Subject Assistance: Inter- and Intra-Subject Maximal Correlation for Enhancing the Performance of SSVEP-Based BCIs

Sun

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2021

“…In addition to the previously mentioned algorithms, machine-learning algorithms are widely used in the SSVEP-BCI system. The commonly used classification algorithms in the SSVEP-BCI system include the following categories: naive Bayesian model [55,56], decision-tree model [57][58][59], support-vector machine (SVM) [60][61][62][63], k-nearest neighbor (KNN) [64][65][66], logistic regression [67][68][69][70], and ensemble learning [71][72][73]. The Bayesian model is based on Bayesian principle and uses the knowledge of probability and statistics to classify a sample data set.…”

Section: Machine-learning Algorithmmentioning

confidence: 99%

Review of brain–computer interface based on steady‐state visual evoked potential

Liu

Zhang

Liu

et al. 2022

Brain Science Advances

The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

“…The main idea of FRT is to transfer the stationary features across different source domains and adjust the discriminative features for the target domain ( Sybeldon et al, 2017 ; Yin et al, 2017 ; Hossain et al, 2018 ; Jin et al, 2018 ).…”

Section: Transfer Learningmentioning

confidence: 99%

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Huang

Hua

et al. 2021

Front. Neurosci.

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.