Abstract
The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.
Highlights
Brain-computer interface (BCI) is a direct communicationcontrol system which establishes a transmission channel between electrical signals of a human’s brain and external devices without the involvement of muscles and peripheral nervous system [1]
The high signal-to-noise ratio (SNR), high information transfer rate (ITR), and little training required can be achieved by the state visual evoked potential- (SSVEP-)based BCI system [13]. e state visual evoked potential (SSVEP) is a periodic response of brain which is reflected by repetitive visual stimuli flickering with a certain
Offline Experimental Results. e offline experiment aims to find the optimal parameter for online SSVEP recognition
Summary
Brain-computer interface (BCI) is a direct communicationcontrol system which establishes a transmission channel between electrical signals of a human’s brain and external devices without the involvement of muscles and peripheral nervous system [1]. BCI techniques have been increasingly developed by utilizing neurophysiological signals, such as EEG, magnetoencephalography (MEG), near-infrared spectroscopy (NIRS), and functional magnetic resonance imaging (fMRI) [2,3,4]. BCI can improve the quality of life of disabled people and can provide additional help and entertainment mode for healthy people to achieve multifunctional augmentative and alternative tasks [6]. Nowadays several basic paradigms have been utilized to realize EEG-based BCIs, such as event-related potential (ERP) [7], P300 potential [8], steady-state visual evoked potential (SSVEP) [9, 10], slow cortical potential (SCP) [11], and motor imagery (MI) [12]. The high signal-to-noise ratio (SNR), high ITR, and little training required can be achieved by the SSVEP-based BCI system [13]. e SSVEP is a periodic response of brain which is reflected by repetitive visual stimuli flickering with a certain
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