Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Behav Brain Res. 2024 Feb 29;459:114760. doi: 10.1016/j.bbr.2023.114760. Epub 2023 Nov 17.
Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.
脑机接口（BCIs）在操作中高度依赖运动想象（MI），但触觉想象（TI）提供了一种新颖的方法，在视觉反馈不切实际的情况下可能具有优势。本研究旨在比较 TI 和 MI 引起的皮层活动和数字分类性能，以评估 TI 在 BCIs 中的应用可行性。12 名右利手参与者参与了 TI 和 MI 的试验，重点是他们的左手和右手食指。分析了与事件相关的去同步（ERD）在 mu 和 beta 波段，并通过人工神经网络（ANN）确定分类准确性。在 TI 和 MI 中均观察到可比的 ERD 模式，在想象任务中 ERD 显著降低。ANN 表现出高分类准确性，TI 的平均准确率±SD 为 79.30±3.91%，MI 的平均准确率±SD 为 81.10±2.96%，两者之间无显著差异（p=0.11）。该研究发现，TI 可引起与 MI 相当的大量 ERD，并保持高分类准确性，支持其作为 BCIs 有效心理策略的潜力。这表明 TI 可能是 BCI 应用中的一种有价值的替代方法，特别是对于无法依赖视觉提示的个体。
