This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink  device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left,… (More)
— An unsupervised adaptive Gaussian mixture model is introduced for online brain-computer interfaces (BCI). The method is tested on two BCI data sets, demonstrating significant performance improvement in comparison with a static model.
In this paper we present a sequential expectation maximiza-tion algorithm to adapt in an unsupervised manner a Gaussian mixture model for a classification problem. The goal is to adapt the Gaussian mixture model to cope with the non-stationarity in the data to classify and hence preserve the classification accuracy. Experimental results on synthetic data… (More)
—In this paper, a new scheme for constructing parsimonious fuzzy classifiers is proposed based on the L2-support vector machine (L2-SVM) technique with model selection and feature ranking performed simultaneously in an integrated manner, in which fuzzy rules are optimally generated from data by L2-SVM learning. In order to identify the most influential… (More)
Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually… (More)
This paper investigates manifestation of fatigue in myoelectric signals during dynamic contractions produced whilst playing PC games. The hand's myoelectric signals were collected in 26 independent sessions with 10 subjects. Two methods, spectral analysis and time-scale analysis, were applied to compute signal frequency and least-square linear regression… (More)
SUMMARY: Unsupervised manifold learning for dimensionality reduction has drawn much attention in recent years. This paper applies two manifold learning methods for the first time to feature dimensionality reduction in brain-computer interface (BCI) design, and compares them with principal component analysis (PCA) and supervised PCA that is mathematically… (More)