汉语言语想象神经机制及其脑机接口应用研究

言语想象脑机接口（brain computer interfaces，BCI）使运动障碍患者能够以自然、用户友好的方式将他们的想法和意图传达给外界，在医学康复和神经工程领域具有广阔的应用前景。功能性近红外成像（functional near-infrared spectroscopy，fNIRS）由于其无创、便携、安全、对运动伪影不敏感以及具有相对较高的空间分辨率等优点，被认为是开发BCI系统最合适的脑成像方法之一。然而，由于血流动力学反应相对缓慢，基于fNIRS的BCI的命令生成时间通常至少需要10 s，使其命令生成时间相对较长。尽管基于fNIRS的BCI已经发展了十几年，但其解码性能仍有待进一步提高，并且与fNIRS相应的算法研究也相对缺乏。

为了解决这些问题，本文开展了减少基于fNIRS的言语想象BCI的命令生成时间和提高其解码性能的研究。具体研究内容如下：

首先，为了检测用户与BCI系统交互的意图，本文提出了一种基于fNIRS的异步BCI系统检测简化的发音器官（下颌和嘴唇）运动想象。在神经机制上，本文发现简化的发音器官运动想象会激活双侧运动前区与辅助运动皮层。在方法上，本文首次探究了将功能连接网络分析方法与共空间方法应用于基于fNIRS的言语想象检测的可行性，并提出了一种特征结合算法以结合时域、空域和功能连接特征，提升言语想象检测性能。该方法利用0-2.5 s时间窗内fNIRS信号的早期信息检测言语想象，相对于常用的0-10 s时间窗显著缩短了检测时间。因此，该研究不仅缩短了基于fNIRS的言语想象BCI的任务检测时间而且提高了其任务状态检测精度。

第二，为了解码想象的元音，本文提出了一种基于fNIRS的元音想象解码模型，该模型利用简化的发音器官运动想象（仅保留下颌和嘴唇的运动）信息解码4个想象的元音。在神经机制上，本文发现运动复杂度和运动强度越高的元音发音器官运动想象在运动皮层诱发的神经反应越强。在方法上，本文探究了将功能连接网络分析方法应用于基于fNIRS的言语想象BCI的可行性。功能连接特征的分类准确率高于常用的时域特征和基于矢量的相位分析特征的分类准确率且特征提取的时间窗从0-10 s缩短到0-2.5 s不会显著降低其分类准确率。因此，该研究不仅提升了基于fNIRS的言语想象BCI的元音解码精度，且缩短了其命令生成时间。

第三，为了解码更多的想象语音单元并显著缩短想象汉语单词的拼写时间，本文提出了一种基于fNIRS的元音与声调想象解码模型。在神经机制上，本文首次发现声调想象与元音想象分别在右半球和左半球诱发更多的神经反应且音高变化越多的声调想象诱发的神经反应越强。在方法上，本文探究了应用功能连接网络分析方法同时解码想象的元音与声调的可行性。功能连接特征在0-2.5 s 时间窗的分类准确率高于时域特征和相位分析特征的分类准确率。因此，该研究显著缩短了基于fNIRS的言语想象BCI拼写器拼写汉语单词的时间且提升了解码准确率。

最后，为了提高基于fNIRS的言语想象BCI的分类性能，本文提出了一种新的言语想象实验范式。根据言语想象的神经机制，本文通过改变声调想象和简化元音发音器官运动想象改进传统言语想象范式，提出了一种新的言语想象范式。该范式可以使不同言语想象任务之间的神经活动更加具有区分性，从而显著提高基于fNIRS的言语想象BCI的解码性能。

综上，本文根据言语想象神经机制改善了言语想象范式并提出了新的算法提升基于fNIRS的言语想象BCI的解码准确率，且利用fNIRS信号的早期信息显著减少基于fNIRS的BCI的命令生成时间。

Speech imagery brain computer interfaces (BCIs) empower people with motor disorders to communicate their thoughts and intentions to their environment in a natural, user-friendly way, and have great prospects in the fields of medical rehabilitation and neural engineering. Functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, safe, insensitive to motion artifacts, and having relatively high spatial resolution. However, due to the relatively slow hemodynamic response, the command generation time of BCI based on fNIRS usually takes at least 10 s which is too long for BCIs. Although speech imagery BCIs based on fNIRS have been developed for many years, their decoding performances still need further improvement, and fNIRS-specific algorithm research is relatively lacking.

To address those issues, this dissertation studied reducing command generation time and improving the decoding performance of speech imagery BCIs based on fNIRS. Specific researches are listed as follows:

Firstly, in order to detect the user's intention to interact with a BCI system, an asynchronous BCI based on fNIRS that could detect simplified articulation (jaw and lips) motor imagery was developed. Bilateral premotor cortices and supplementary motor cortex were found activated by simplified articulation motor imagery. The present study first explored the feasibility of applying functional connectivity analysis methods and the common spatial pattern method to fNIRS-based speech imagery detection and proposed a feature fusion strategy that combined the time domain, spatial domain, and functional connectivity features to improve speech imagery detection accuracy. Speech imagery was detected by using early information of fNIRS signals in 0-2.5 s time window, which is significantly shorter than the commonly used 0-10 s time window. Therefore, the present study not only improved the task detection accuracy but also reduced the task detection time for speech imagery BCI based on fNIRS.

Secondly, in order to decode imagined vowels, a vowel imagery decoding model based on fNIRS was presented by using simplified articulation movements imagery (only the movements of jaw and lip were retained) information. Articulation motor imagery with higher motor complexity and motor intensity was found to elicit stronger neural responses in the motor cortex. The present study explored the feasibility of applying functional connectivity analysis methods for the fNIRS-based speech imagery BCI. The classification performance of functional connectivity features was better than that of the conventional temporal features and features acquired from vector-based phase analysis, and it would not decrease due to reducing the time window from 0-10 s to 0-2.5 s. Therefore, the present study improved the decoding accuracy and reduced the command generation time of speech imagery BCIs based on fNIRS.

Thirdly, in order to decode more imagined phonetic units and significantly reduce the spelling time of imagined Chinese words, a vowel and tone imagery decoding model based on fNIRS was presented. Tone imagery and vowel imagery were found to elicit more neural responses in the right and left hemispheres, respectively, and tone imagery with more pitch variation was found to elicit stronger neural responses. The present study explored the feasibility of applying functional connectivity analysis methods to decode the imagined vowels and tones simultaneously. The classification performance of functional connectivity features was better than that of the conventional temporal features and features acquired from vector-based phase analysis in the 0-2.5 s time window. Therefore, the present study improved the decoding accuracies of imagined vowels and tones and significantly reduced the spelling time required for speech imagery BCI speller based on fNIRS to spell Chinese words.

Finally, in order to improve the classification performance of speech imagery BCIs based on fNIRS, a novel paradigm was developed. Based on the neural mechanisms of speech imagery, a new speech imagery paradigm was presented by modifying imagined lexical tone and simplifying articulation motor imagery of the traditional speech imagery paradigm. The new paradigm could make brain activation more distinguishable between different speech imagery tasks to significantly improve the decoding performance of speech imagery BCIs based on fNIRS.

In summary, the decoding accuracy of speech imagery BCI based on fNIRS was enhanced by improving the speech imagery paradigm based on the speech imagery mechanism and applying novel algorithms, and the command generation time of fNIRS-based BCI was significantly reduced by using early information.

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