IEEE Trans Neural Syst Rehabil Eng. 2018 Jan;26(1):3-10. doi: 10.1109/TNSRE.2017.2731261. Epub 2017 Jul 24.
We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile-induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L); 2) selective sensation on right hand (SS-R); 3) bilateral selective sensation; and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9 ± 8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3 ± 4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had an accuracy of 75.2 ± 8.3%, with the best discrimination reached for the classes SS-L, SS-R, and SS-S. Finally, four classes were classified with an accuracy of 59.4 ± 7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.
我们提出了一种利用刺激诱导的振荡动力学的多类触觉脑机接口。假设感觉注意可以调节触觉诱导的振荡变化，从而可以解码不同的感觉注意任务。受试者执行四个触觉注意任务，提示随机呈现，同时刺激两个手腕：1）左手选择性感觉（SS-L）；2）右手选择性感觉（SS-R）；3）双侧选择性感觉；4）选择性感觉抑制或空闲状态（SS-S）。SS-L 和 SS-R 之间的分类准确率（79.9±8.7%）与基于选择性感觉的先前触觉 BCI 系统相当。此外，通过最优类对和频带选择，准确率可提高到平均 90.3±4.9%。三分类的准确率为 75.2±8.3%，最佳分类为 SS-L、SS-R 和 SS-S。最后，准确率为 59.4±7.3%，可对四种类别进行分类。这些结果表明，所提出的系统是一种有前途的多类脑机接口新范式。
