Multiway-or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector-or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector-or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.

中文翻译：

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使用具有快速和自动分量提取的块项张量回归从颅内脑活动预测单指轨迹。

与传统的基于矢量或矩阵的解码技术相比，用于脑机接口 (BCI) 的多路或基于张量的解码技术被认为可以更好地解释大脑信号的多线性结构。然而，尽管他们期待显着的性能提升，但所使用的参数优化方法通常对计算要求太高，因此传统技术仍然是首选。我们提出了两个新的张量分解，我们将其整合到我们的块项张量回归 (BTTR) 算法中，并进一步引入边缘化程序，以保证稳健的预测，同时降低过度拟合的风险（广义回归）。BTTR 以全自动和计算高效的方式说明底层（隐藏）数据结构，在具有挑战性的实际应用中，与传统的基于矢量或矩阵的技术相比，性能显着提高。作为一项具有挑战性的现实应用，我们应用 BTTR 从人类受试者的颅内记录中准确预测单指运动轨迹。我们将获得的性能与最先进的性能进行比较。