一种新的混合脑机接口多通道方法
A novel multimodal approach for hybrid brain-computer interface
摘要：
脑机接口（Brain-computer interface，BCI）技术在许多领域得到了广泛的应用。特别是，非侵入性技术，如脑电图（EEG）或近红外光谱（NIRS）已被用于检测运动想象，疾病或精神状态。已有文献表明，EEG和NIRS的混合比它们各自的单独信号具有更好的结果。脑电信号和近红外光谱信号的融合算法是实现两者在实际应用中的关键。在本研究中，我们提出了三种融合方法的混合的EEG和NIRS的脑机接口系统：线性融合，张量融合，和$p$次多项式融合。首先，我们的结果证明混合BCI系统更准确，正如预期的那样。其次，$p$阶多项式融合的分类效果最好，与以往的研究相比也有一定的改进。对于运动想象任务和心算任务，以往文献的最佳检测准确率分别为74.20%和88.1%，而我们的检测准确率分别为77.53%和90.19%。此外，与复杂的人工神经网络方法不同，我们提出的方法在计算上要求不高。
英文摘要：
Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-computer interface system: linear fusion, tensor fusion, and $p$th-order polynomial fusion. Firstly, our results prove that the hybrid BCI system is more accurate, as expected. Secondly, the $p$th-order polynomial fusion has the best classification results out of the three methods, and also shows improvements compared with previous studies. For a motion imagery task and a mental arithmetic task, the best detection accuracy in previous papers were 74.20\% and 88.1\%, whereas our accuracy achieved was 77.53\% and 90.19\% . Furthermore, unlike complex artificial neural network methods, our proposed methods are not as computationally demanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
