Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily operate. Imagined speech and visual imagery are emerging paradigms that can directly convey a user’s intention. We investigated the underlying characteristics that affect the decoding performance of these two paradigms. Twenty-two subjects performed imagined speech and visual imagery of twelve words/phrases frequently used for patients’ communication. Spectral features were analyzed with thirteen-class classification (including rest class) using EEG filtered in six frequency ranges. In addition, cortical regions relevant to the two paradigms were analyzed by classification using single-channel and pre-defined cortical groups. Furthermore, we analyzed the word properties that affect the decoding performance based on the number of syllables, concrete and abstract concepts, and the correlation between the two paradigms. Finally, we investigated multiclass scalability in both paradigms. The high-frequency band displayed a significantly superior performance to that in the case of any other spectral features in the thirteen-class classification (imagined speech: 39.73 ± 5.64%; visual imagery: 40.14 ± 4.17%). Furthermore, the performance of Broca’s and Wernicke’s areas and auditory cortex was found to have improved among the cortical regions in both paradigms. As the number of classes increased, the decoding performance decreased moderately. Moreover, every subject exceeded the confidence level performance, implying the strength of the two paradigms in BCI inefficiency. These two intuitive paradigms were found to be highly effective for multiclass communication systems, having considerable similarities between each other. The results could provide crucial information for improving the decoding performance for practical BCI applications.

中文翻译：

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想象的语音和视觉图像的神经解码作为BCI通信的直观范例

脑机接口（BCI）面向用户可以轻松操作的直观系统。想象的语音和视觉图像是可以直接传达用户意图的新兴范例。我们研究了影响这两个范例的解码性能的基本特征。22名受试者进行了想象中的语音和视觉图像，其中包括十二个单词/短语，经常用于患者交流。使用在六个频率范围内过滤的EEG，通过13类分类（包括静止类）分析了光谱特征。此外，使用单通道和预定义的皮质组通过分类分析了与这两种范例相关的皮质区域。此外，我们根据音节的数量分析了影响解码性能的字属性，具体和抽象的概念，以及这两种范式之间的相互关系。最后，我们研究了两种范例中的多类可伸缩性。该高频段显示出的性能明显优于十三类分类中的任何其他频谱特征（想象的语音：39.73±5.64％；视觉图像：40.14±4.17％）。此外，发现两种范例的皮质区域之间的Broca和Wernicke区域以及听觉皮层的性能均得到改善。随着类别数量的增加，解码性能会适度下降。而且，每个受试者都超过了置信水平的表现，这暗示了BCI效率低下这两种范式的优势。发现这两种直观的范例对于多类通信系统非常有效，彼此之间有很多相似之处。结果可为提高实际BCI应用的解码性能提供关键信息。