IEEE Trans Neural Syst Rehabil Eng. 2022;30:85-95. doi: 10.1109/TNSRE.2022.3140772. Epub 2022 Jan 28.
This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.
本研究评估了在小尺寸增强现实（AR）头显中，背景变化对基于稳态视觉诱发电位（SSVEP）和稳态运动视觉诱发电位（SSMVEP）的脑机接口（BCI）的影响。使用 Cognixion AR 头显原型实现了一个具有四个目标的 SSVEP 和 SSMVEP BCI。评估了一个活动（AB）和一个非活动（NB）背景。研究了两种 BCI 范式的信号特征和分类性能。离线分析使用典型相关分析（CCA）和基于复谱的卷积神经网络（C-CNN）进行。最后，评估了 SSMVEP BCI 的异步伪在线性能。信号分析表明，与 SSVEP 刺激相比，AR 中的 SSMVEP 刺激对背景变化更具鲁棒性。解码性能表明，CCA 方法在两种刺激类型和 NB 背景下均优于 C-CNN 方法，这与文献中的结果一致。对于 C-CNN 方法，W = 1 s 的离线平均准确率为（NB 与 AB）：SSVEP：82% ±15%与 60% ±21%，SSMVEP：71.4% ± 22%与 63.5% ± 18%。此外，对于 W = 2 s，使用 C-CNN 方法的 AR-SSMVEP BCI 在 NB 条件下的准确率为 83.3% ± 27%，AB 条件下的准确率为 74.1% ±22%。结果表明，与 AR-SSVEP BCI 相比，使用 C-CNN 方法的 AR-SSMVEP BCI 对背景条件变化具有更强的鲁棒性，并且提供了更高的解码准确性。本研究提出了新的结果，强调了使用低成本 AR 头显开发的 SSMVEP BCI 的鲁棒性和实际应用。
