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首页> 外文期刊>Computational intelligence and neuroscience >Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control
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Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control

机译：基于信道投影的基于SSVEP的BCI系统的CCA目标识别方法

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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.
机译：脑电脑界面（BCI）在辅助肌营养的侧面硬化（ALS）患者方面发挥着重要作用，使他们能够参与沟通和娱乐。在该研究中，提出了一种基于新的基于信道投影的CANONELICE相关性分析（CP-CCA）基于稳态视觉诱发电位 - （SSVEP-）的BCI系统的规范识别方法。当受试者处于相同的刺激频率时，记录多声道脑电图（EEG）信号。获得了多次试验和正弦余弦参考信号的单通道EEG信号之间的CCA。然后，利用每个信道的最佳参考信号来估计测试EEG信号。为了验证所提出的方法，我们在具有两个测试条件的训练数据集中获得了包括最佳时间窗口的长度和训练数据的试验的数量。离线实验进行了使用从10个受试者记录的5级SSVEP数据集进行传统的规范相关分析（CCA）和功率谱密度分析（PSDA）方法的提出方法的比较。基于培训数据集，进行了在线3D直升机控制实验。离线实验结果表明，该方法在分类准确性和信息转移率（ITR）方面表现出CCA和PSDA方法。此外，3-DOF直升机控制的在线实验实现了87.94的平均精度为87.94？±5.93％，ITR为21.07？±4.42？位/分钟。

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