Chi Man Wong

Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces

BackgroundThe fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users’ discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs.MethodsHow the EEG indices (amplitudes in δ, θ, α and β frequency bands), the selected ratio indices (θ/α and (θ + α)/β), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index.ResultsConsistent results are obtained in two experiments: the significant increases in α and (θ + α)/β, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users’ fatigue.ConclusionsThe experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.

A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection

Minimum energy combination (MEC) and canonical correlation analysis (CCA) are widely used for steady-state visual evoked potential (SSVEP) based brain computer interface (BCI), since both approaches have satisfactory performance. The purpose of this paper is to provide a guideline on choice of detection method, through comparison of the performance of the two approaches from simulation data and real SSVEP data. The experiment results show that CCA has lower deviation, higher accuracy and higher signal to noise ratio than MEC.

Comparison of different classification methods for EEG-based brain computer interfaces: A case study

The performances of different off-line methods for two different Electroencephalograph (EEG) signal classification tasks - motor imagery and finger movement, are investigated in this paper. The classifiers based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kernel fisher discriminant (KFD), support vector machine (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) neural network, k-nearest neighbor (k-NN), and decision tree (DT), are compared in terms of classification accuracy. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. As a result, a guideline for choosing appropriate algorithms for EEG classification tasks is provided.

An improved phase-tagged stimuli generation method in steady-state visual evoked potential based brain-computer interface

This paper proposes an improved method for generating different phases of visual stimulus while liquid crystal display (LCD)/cathode ray tube (CRT) is employed as the visual stimulator. Since using the traditional method can only generate the limited frequencies and phases of visual stimulus, increasing the number of different flickering targets becomes very difficult in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The experimental result shows that the proposed method can generate the visual stimulus with more phase angles than the traditional method. In addition, the proposed LCD visual stimulator can evoke the subjects SSVEP with expected phase.

Adaptive Fourier decomposition based ECG denoising

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition.

A high rate online SSVEP based brain-computer interface speller

This paper presents an online steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). Stimuli are displayed on a liquid crystal display (LCD) screen with a frame based encoding method to elicit SSVEPs with a wide range of frequencies. This system focuses strongly on practicability and convenience, including an adequate alphabet (42 characters) that can allow a wide range of options. Four healthy subjects attain a mean information transfer rate (ITR) of 61.64±3.61 bits/min.

Muscle and electrode motion artifacts reduction in ECG using adaptive Fourier decomposition

The reduction of the muscle and electrode motion artifacts in ECG using the adaptive Fourier decomposition (AFD) is investigated. This is an extension of our previous work, in which AFD is first proposed for ECG denoising and its effectiveness in filtering out the additive Gaussian white noise is tested. This paper studies the AFD-based ECG denoising method for two types of ECG noise due to the electrode movement and the muscle contraction which are common and important in practice. In addition, some rules on the selection and adjustment of the AFD decomposition level are proposed. The tests on the MIT-BIH Arrhythmia Database indicate that this AFD-based denoising scheme performs better than the Butterworth lowpass filter, the wavelet transform and the empirical mode decomposition methods for ECG denoising with the muscle movement and electrode motion artifacts.

Trial pruning based on genetic algorithm for single-trial EEG classification

We consider the problem of artifacts in electroencephalography (EEG) data. In a practical motor imagery based brain-computer interface (BCI) system, EEG signals are usually contaminated by misleading trials caused by artifacts, measurement inaccuracies, or improper imagination of a movement. As a result, the performance of a BCI system can be degraded. In this paper, we introduce a novel algorithm combining Gaussian mixture model (GMM) and genetic algorithm (GA) to detect the abnormal EEG samples. In addition, this algorithm can be also integrated with other data-driven feature exaction method (e.g., common spatial pattern (CSP)) so that a more reliable analysis can be obtained by pruning the potential outliers and noisy samples, and consequently the performance of a BCI system can be improved. Experimental results demonstrate significant improvement in comparison with the conventional mixture model.

Gaussian mixture model based on genetic algorithm for brain-computer interface

Gaussian mixture model (GMM) has been considered to model the EEG data for the classification task in brain-computer interface (BCI) system. In the practical BCI application, however, the performance of the classical GMM optimized by standard expectation-maximization (EM) algorithm may be degraded due to the noise and outliers, which often exist in realistic BCI systems. The motivation of this paper is to introduce the GMM based on the combination between the genetic algorithm (GA) and EM method to give a probabilistic output for further analysis and, more important, to achieve the reliable estimation by pruning the potential outliers and noisy samples in the EEG data, so the performance of BCI system can be improved. Experiments on two BCI datasets demonstrate the improvement in comparison with the classical mixture model.

Fast Basis Searching Method of Adaptive Fourier Decomposition Based on Nelder-Mead Algorithm for ECG Signals

The adaptive Fourier decomposition AFD is a greedy iterative signal decomposition algorithm in the viewpoint of energy. Instead of using a fixed basis for decomposition, AFD uses an adaptive basis to achieve efficient energy extraction. In the conventional searching method, a new basis is searched from a large dictionary at every decomposition level. This usually results in a slow searching speed. To improve the efficiency, a fast searching method based on Nelder-Mead algorithm is proposed in this paper. The AFD with the proposed searching method is applied for electrocardiography ECG signals in which the selection ranges of four key parameters in the proposed searching method are determined based on simulation results of an artificial ECG signal. The simulation results of real ECG data shows that the computational time of the AFD based on the proposed searching method is just half of that based on the conventional searching method with similar reconstruction error.