Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W. EV-009.187, Montreal, QC H3G 1M8, Canada.
Concordia Institute for Information System Engineering, Concordia University, 1455 De Maisonneuve Blvd. W. EV-009.187, Montreal, QC H3G 1M8, Canada.
Sensors (Basel). 2022 Mar 27;22(7):2568. doi: 10.3390/s22072568.
Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.
近年来，脑电（EEG）传感器技术和信号处理算法的进步为脑机接口（BCI）在许多实际应用中的进一步发展铺平了道路，从康复系统到智能消费技术。在脑机接口的信号处理（SP）方面，稳态运动视觉诱发电位（SSmVEP）引起了极大的兴趣，其中运动刺激被用于解决与传统光闪烁/闪烁相关的关键问题。然而，这些好处带来了准确性较低和信息传输率（ITR）较低的代价。从这个角度来看，本文专注于设计一种新颖的 SSmVEP 范式，而无需使用试验时间、相位和/或目标数量等资源来提高 ITR。所提出的设计基于同时在单个 SSmVEP 目标刺激中集成多个运动的直观想法。为了引出 SSmVEP，我们设计了一种新颖的双频聚合调制范式，称为双频聚合稳态运动视觉诱发电位（DF-SSmVEP），通过在单个目标中同时集成“径向缩放”和“旋转”运动，而不增加试验长度。与传统的 SSmVEP 相比，所提出的 DF-SSmVEP 框架由两个运动模式组成，每个模式都由特定的目标频率调制，并同时显示。该文还开发了一种特定的无监督分类模型，称为双折叠典型相关分析（BCCA），该模型基于每个目标的两个运动频率。相应的协方差系数被用作额外的特征，以提高分类准确性。基于真实 EEG 数据集评估了所提出的 DF-SSmVEP，结果证实了其优越性。所提出的 DF-SSmVEP 优于其同类产品，平均 ITR 为 30.7 ± 1.97，平均准确率为 92.5 ± 2.04，而径向缩放和旋转的平均 ITR 分别为 18.35 ± 1 和 20.52 ± 2.5，平均准确率分别为 68.12 ± 3.5 和 77.5 ± 3.5。
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