A significant problem in brain–computer interface (BCI) research is decoding — obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83–78.86% (median) was achieved during GVS for every 100ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.

中文翻译：

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基于电流前庭刺激的预测误差解码和通道优化

脑机接口 (BCI) 研究中的一个重要问题是解码——从非常微弱的噪声脑电图信号中获取所需的信息，并从有限的数据中提取大量信息。传统的意图解码方法从诱导或自发的大脑活动中获取信息，在性能、计算成本和使用负担方面存在缺陷。在这里，一种称为预测误差解码的新方法被用于运动图像 (MI) 检测，并与直接意图解码进行了比较。前庭电刺激 (GVS) 用于诱导前额和乳突之间的潜意识感觉反馈，没有任何负担。在 GVS 诱导的感觉反馈和 MI 方向之间产生了预测误差。验证了前/后MI任务的相应预测误差解码。在 GVS 期间，每 100 个测试解码精度达到 77.83–78.86%（中位数）毫秒间隔。提出了一种基于非零权重参数的通道筛选 (WPS) 方法，用于在 GVS 期间单独且普遍地选择通道。当将 WPS 共选模式与 WPS 单选模式和基于相关系数 (CCS) 的经典频道选择方法进行比较时，观察到所选频道的解码性能令人满意。结果表明，测量 BCI 的常见特定通道具有积极影响。