Abstract
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.
Highlights
An electroencephalogram (EEG) is a test during which the electric signals of the brain are measured from the scalp of the user
We propose a new method that combines an EEG acquisition device and a frontal viewing camera to detect the EEG signals which include the noises caused by the user’s head movements
We compared the accuracies in detecting the head movements based on the features of the EEG signals in the frequency and time domains and the motion features of the images captured by the frontal viewing camera
Summary
An electroencephalogram (EEG) is a test during which the electric signals of the brain are measured from the scalp of the user. An EEG-based brain–computer interface (BCI) has been widely researched for various applications, including intuitive control for computers, mobile devices, wheelchairs, and robots, as well as for the diagnosis of cerebropathy and for the indirect measurement of the cognitive, emotional, and psychological status of the user [1,2,3,4,5,6]. In a previous research study, navigation in 3D applications was performed by combining gaze tracking and an EEG measuring device [4]. Another study showed that the user’s EEG signals can be used to control electric wheelchairs [2,8]. A study showed the results of controlling a BCI speller system based on the steady-state visually evoked potential (SSVEP) [9]
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