CNNATT：深度EEG & fNIRS双手力实时的解码
CNNATT: Deep EEG & fNIRS Real-Time Decoding of bimanual forces
摘要：
与侵入性脑机接口（BCI）相比，非侵入性皮层神经接口在肢体运动及其力量的皮层解码中仅实现了适度的性能。虽然非侵入性方法是更安全，更便宜和更容易获得的技术，但信号在空间域（EEG）或时间域（功能性近红外光谱，fNIRS的BOLD信号）中的分辨率较差。双手力产生和连续力信号的非侵入性BCI解码以前还没有实现，所以我们引入了等距握力跟踪任务来评估解码。我们发现，使用深度神经网络结合EEG和fNIRS比线性模型更好地解码左手和右手产生的连续握力调制。我们的多模态深度学习解码器在力重建方面实现了55.2 FVAF[%]，并在每个模态上将解码性能提高了至少15%。我们的研究结果表明，使用非侵入性移动脑成像获得的皮层信号实现连续手部力量解码的方法对康复，恢复和消费者应用具有直接影响。
英文摘要：
Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer, cheaper and vastly more accessible technologies, signals suffer from either poor resolution in the space domain (EEG) or the temporal domain (BOLD signal of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding of bimanual force generation and the continuous force signal has not been realised before and so we introduce an isometric grip force tracking task to evaluate the decoding. We find that combining EEG and fNIRS using deep neural networks works better than linear models to decode continuous grip force modulations produced by the left and the right hand. Our multi-modal deep learning decoder achieves 55.2 FVAF[%] in force reconstruction and improves the decoding performance by at least 15% over each individual modality. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.
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