Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain–computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bit min−1. The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.

https://iopscience.iop.org/article/10.1088/1741-2560/6/4/046002/pdf

An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method

The past 20 years have witnessed unprecedented progress in brain-computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.

https://www.pnas.org/content/pnas/112/44/E6058.full.pdf

High-speed spelling with a noninvasive brain–computer interface

Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

/pdf/a-survey-of-stimulation-methods-used-in-ssvep-based-bcis-38omyw8mqs.pdf

A survey of stimulation methods used in SSVEP-based BCIs

https://www.cs.tau.ac.il/~nin/Courses/Seminar14a/P300.pdf

How many people are able to control a P300-based brain-computer interface (BCI)?

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method

Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

Rapid and brief communication: Exploring the structure of supervised data by Discriminant Isometric Mapping

Network Boosting is an ensemble learning method which combines learners together based on a network and can learn the target hypothesis asymptotically. We apply the approach to analyze data from the P300 speller paradigm. The result on the Data set II of BCI (Brain-computer interface) competition III shows that Network Boosting achieves higher classification accuracy than logistic regression, SVM, Bagging and AdaBoost.

Network boosting for BCI applications

In researches and experiments, we often work with large volumes of high-dimensional data and regularly confront the problem of dimensionality reduction. Some Non-Linear Dimensionality Reduction (NLDR) methods have been developed for unsupervised datasets. As for supervised datasets, there is a newly developed method called SIsomap, which is capable of discovering structures that underlie complex natural observations. However, SIsomap is limited from the fact that it doesn’t provide an explicit mapping from original space to embedded space, and thus can’t be applied to classification. To solve this problem, we apply neural network to learning that mapping and then to classify. We test our method on a real world dataset. To prove the effectiveness of our method, we also compare it with a related classification method, Extended Isomap. Experiments show that our proposed method has satisfactory performance.

Learning the Supervised NLDR Mapping for Classification

Canonical Correlation Analysis (CCA) is used to frequency recognition of multichannel signals. The unknown signals are compared against known templates and their frequencies are recognized by simply comparing the biggest coefficients of their CCA coefficient vectors. This strategy is straightforward but may not give optimal results. To boost the accuracy of recognition we reformulate the approach in views of machine learning. In this paper, we propose a new strategy based on supervised learning. We also employ feature selection within this framework to adopt efficient coefficients which may not be the largest coefficients for the features vectors. The recognition method is validated by results with real world data.

Zhonglin Lin

Papers

Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs

Rapid and brief communication: Exploring the structure of supervised data by Discriminant Isometric Mapping

Network boosting for BCI applications

Learning the Supervised NLDR Mapping for Classification

Machine Learning Way for Boosting Accuracy in Canonical Correlation Analysis based Frequency Recognition