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In this paper, a steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) system using multiple channel electroencephalography (EEG) signals is designed for computer games that require analogue or continuous control. To this end, a novel scheme combining two SSVEP's features is proposed to control the simulated cart in a relatively continuous and accurate way. More specifically,...
Electroencephalogram (EEG) signals are often contaminated with various artifacts, especially electrooculogram (EOG) or ocular artifacts that cannot be avoided consciously and largely degrade the clinical interpretation of the signals. This paper presents a study on adaptive noise cancellation (ANC) based on adapüve neuro-fuzzy inference system (ANFIS) for EOG artifacts removal, especially when time...
Our previous study successfully improved short term memory by individual alpha neurofeedback training. However, short term memory and its improvement in different languages are still not clear. Therefore, the purpose of this paper was to evaluate the memory performance in alphabetic and ideographic language groups. Besides, the relationship between initial memory and its improvement was investigated...
A wearable wireless general purpose bio-signal acquisition prototype system is presented in this paper. Three types of bio-signals could be acquired by the system then wirelessly transmitted to the computer for real-time processing. The prototype experimental results show that the system could acquire electrocardiogram (ECG), surface-electromyography (s-EMG) and electroencephalogram (EEG) for home...
The performance of a brain computer interface (BCI) system is usually degraded due to the outliers in electroencephalography (EEG) samples. This paper presents a novel outlier detection method based on robust learning of Gaussian mixture models (GMMs). We apply the proposed method to the single-trial EEG classification task. After trial-pruning, feature extraction and classification are performed...
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...
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...
A novel low-power EEG readout front-end featuring a current-mode instrumentation amplifier (CMIA) followed by a 4th-order gain-compensated source-follower-based lowpass filter (LPF) is proposed. The CMIA is of current-conveyor topology and is chopper-stabilized to improve the common-mode noise rejection and suppress the dc-offset and 1/f noise. The typical gain-loss problem of source-follower-based...
Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After...
Classification of electroencephalogram (EEG) is a crucial issue for EEG-based brain computer interface (BCI) system. In this paper, the performances of the Gaussian process classifier (GPC) for three different categories of EEG signals, i.e. steady state visually evoked potential (SSVEP), motor imagery and finger movement EEG data, are investigated. The main purpose of this paper is to explore the...
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...
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