Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain-computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs.

Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces.

This study addresses neural decoding of a code modulated visual evoked potentials (c-VEPs). c-VEP was recently developed, and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster communication speed than existing VEP-based BCIs.

Reliability-based automatic repeat request for short code modulation visual evoked potentials in brain computer interfaces.

We propose two methods to improve code modulation visual evoked potential brain computer interfaces (cVEP BCIs). Most of BCIs average brain signals from several trials in order to improve the classification performance. The number of averaging defines the trade-off between input speed and accuracy, and the optimal averaging number depends on individual, signal acquisition system, and so forth.

Mahalanobis distance on extended Grassmann manifolds for variational pattern analysis.

In pattern classification problems, pattern variations are often modeled as a linear manifold or a low-dimensional subspace. Conventional methods use such models and define a measure of similarity or dissimilarity. However, these similarity measures are deterministic and do not take into account the distribution of linear manifolds or low-dimensional subspaces.

A novel EEG-based spelling system using N100 and P300.

P300-speller is one of the most popular EEG-based spelling systems proposed by Farwell and Donchin. P300-speller has a 6 × 6 character matrix and requires at least 12 flashes to input one character. This restricts increasing of the information transfer rate (ITR) by decreasing the number of flashes.

Adaptive subset kernel principal component analysis for time-varying patterns.

Kernel principal component analysis (KPCA) and its online learning algorithms have been proposed and widely used. Since KPCA uses training samples for bases of the operator, its online learning algorithms require the preparation of all training samples beforehand. Subset KPCA (SubKPCA), which uses a subset of samples for the basis set, has been proposed and has demonstrated better performance with less computational complexity.

Kernel Wiener filter and its application to pattern recognition.

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples.

Blind extraction of global signal from multi-channel noisy observations.

We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable for global signal extraction problem from noisy observations. We developed an estimation algorithm based on alternating iteration and the smart weighted averaging.

Feature extraction using constrained approximation and suppression.

In this paper, we systematize a family of constrained quadratic classifiers that belong to the class of one-class classifiers. One-class classifiers such as the single-class support vector machine or the subspace methods are widely used for pattern classification and detection problems because they have many advantages over binary classifiers. We interpret subspace methods as rank-constrained quadratic classifiers in the framework.

Extraction of steady state visually evoked potential signal and estimation of distribution map from EEG data.

We propose a signal extraction method from multi-channel EEG signals and apply to extract Steady State Visually Evoked Potential (SSVEP) signal. SSVEP is a response to visual stimuli presented in the form of flushing patterns. By using several flushing patterns with different frequency, brain machine (computer) interface (BMI/BCI) can be realized.

Simultaneous pattern classification and multidomain association using self-structuring kernel memory networks.

In this paper, a novel exemplar-based constructive approach using kernels is proposed for simultaneous pattern classification and multidomain pattern association tasks. The kernel networks are constructed on a modular basis by a simple one-shot self-structuring algorithm motivated from the traditional Hebbian principle and then, they act as the flexible memory capable of generalization for the respective classes. In the self-structuring kernel memory (SSKM), any arduous and iterative network parameter tuning is not involved for establishing the weight connections during the construction, unlike conventional approaches, and thereby, it is considered that the networks do not inherently suffer from the associated numerical instability.