EAGER: Investigating the Neural Correlates of Musical Rhythms from Intracranial Recordings

该项目将开发一个离线和实时的脑机接口，以检测人们头脑中想象的节奏，并将这些节奏转化为实际的声音。该项目建立在皮层电图（ECoG）记录技术的研究突破基础上，将想象中的音乐转换为合成声音。该项目的研究人员将从一个专门的人群中招募人员，特别是目前正在佛罗里达杰克逊维尔的马约诊所接受临床评估的顽固性癫痫患者，因此他们为使用基于ECoG记录技术的脑机接口做好了独特的准备。这是一个高度跨学科的项目，将朝着开发一种“大脑音乐合成器”的方向取得进展，这种合成器可能在神经科学和音乐领域产生重大影响，并为严重残疾的人带来创造性的出路和替代性的通信设备，从而改善他们的生活。大多数脑机接口（BCI）使用表面记录的电生理测量，如表面记录的脑电图（EEG）。然而，虽然可以从这些表面技术中提取一些有用的信号，但几乎不可能从这些信号中准确解码出语言等活动中涉及的复杂大脑活动，以及实现自然，透明的思维转换为设备控制所需的细节。相反，诸如ECoG的颅内电极更接近所需脑活动的源，并且可以产生与表面技术相比具有上级空间和频谱特性以及信噪比的信号。研究已经表明，颅内信号可以为运动和语言信号以及BCI控制提供上级解码能力。由于复杂的语言和听觉信号（感知和想象）已经使用颅内活动解码，因此可以想象从颅内信号解码感知和想象的音乐内容。本项目将尝试类似地使用ECoG从颅内信号中解码感知和想象的音乐内容，就像对语言和听觉信号所做的那样。

The project will develop an offline and then a real-time brain computer interface to detect rhythms that are imagined in people's heads, and translate these rhythms into actual sound. The project builds upon research breakthroughs in electrocorticographic (ECoG) recording technology to convert music that is imagined into synthesized sound. The project researchers will recruit from a specialized group of people for this project, specifically patients with intractable epilepsy who are currently undergoing clinical evaluation of their condition at the Mayo Clinic in Jacksonville, Florida, and are thus uniquely prepared to use brain-computer interfaces based on ECoG recording techniques. This is a highly multidisciplinary project that will make progress towards developing a "brain music synthesizer" which could have a significant impact in the neuroscience and musical domains, and lead to creative outlets and alternative communication devices and thus life improvements for people with severe disabilities.Most brain-computer interfaces (BCIs) use surface-recorded electrophysiological measurements such as surface-recorded electroencephalogram (EEG). However, while some useful signals can be extracted from such surface techniques, it is nearly impossible to accurately decode from such signals the intricate brain activity involved in activities such as language with the detail needed to achieve a natural, transparent translation of thought to device control. On the contrary, intracranial electrodes such as ECoG are closer to the source of the desired brain activity, and can produce signals that, compared to surface techniques, have superior spatial and spectral characteristics and signal-to-noise ratios. Research has already shown that intracranial signals can provide superior decoding capabilities for motor and language signals, and for BCI control. Because complex language and auditory signals (both perceived and imagined) have been decoded using intracranial activity, it is conceivable to decode perceived and imagined musical content from intracranial signals. This project will attempt to similarly use ECoG to decode perceived and imagined musical content from intracranial signals as has been done for language and auditory signals.