Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
J Neural Eng. 2023 Jan 24;20(1). doi: 10.1088/1741-2552/acb105.
. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% ± 7.49% and 86.00% ± 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 ± 39.20 bits minand 204.47 ± 37.56 bits min, respectively. Notably, the peak ITR could reach up to 367.83 bits min.This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.
脑机接口 (BCI) 最近在扩展指令集方面取得了重大进展，引起了研究人员的广泛关注。目标和命令的数量是衡量 BCI 解码大脑意图能力的关键指标。目前还没有研究报道过具有 200 多个目标的 BCI 系统。本研究开发了第一个具有 216 个目标的高速 BCI 系统，这些目标是通过脑电图特征（包括 P300、运动视觉诱发电位 (mVEP) 和稳态视觉诱发电位 (SSVEP)）的组合进行编码的。具体来说，混合 BCI 范式使用时频分多址策略，精心标记具有不同时间窗口的 P300 和 mVEP 以及不同频率的 SSVEP 的目标。然后使用任务判别成分分析和线性判别分析对混合特征进行解码。十位受试者参与了离线和在线提示引导拼写实验。另外十位受试者参加了在线自由拼写实验。离线结果表明，mVEP 和 P300 成分在中央、顶叶和枕叶区域较为突出，而最明显的 SSVEP 特征位于枕叶区域。在线提示引导拼写和自由拼写实验结果表明，所提出的 BCI 系统在 216 个目标分类中平均准确率分别为 85.37%±7.49%和 86.00%±5.98%，平均信息传输率 (ITR) 分别为 302.83±39.20 bits min 和 204.47±37.56 bits min，峰值 ITR 高达 367.83 bits min。本研究开发了第一个具有 200 多个目标的高速 BCI 系统，为扩展 BCI 的应用场景提供了可能。
