Accurate Decoding of Imagined and Heard Melodies.
Accurate Decoding of Imagined and Heard Melodies.
复制标题
DOI：
10.3389/fnins.2021.673401
复制
发表时间：
2021
影响因子：
4.3
通讯作者：
Shamma SA
中科院分区：
文献类型：
--
作者：
Di Liberto GM;Marion G;Shamma SA
音乐感知需要人脑处理各种声学和音乐相关的属性。最近的研究使用编码模型来梳理和研究不同的皮层对音乐感知的贡献。为了做到这一点，这些方法研究了时间反应函数，它总结了几分钟数据的神经活动。在这里，我们测试了用脑电图（EEG）评估单个音乐单元（小节）的神经处理的可能性。我们设计了一种基于EEG段最大相关度量（maxCorr）的解码方法，并基于专业音乐家多次聆听和想象四首巴赫旋律的实验，使用它来解码EEG中的旋律。我们在这里证明，从聆听和想象过程中记录的脑电图信号，对单个受试者和单个音乐单位的旋律进行准确解码是可能的。此外，我们发现maxCorr方法比基于后向时间响应函数（bTRFenv）的包络重建方法具有更高的解码精度。这些结果表明，低频神经信号编码的信息不仅仅是音符时间，特别是1 Hz以下的低频皮层信号，它们编码的是与音高相关的信息。随着这些结果的理论意义，我们讨论了这种解码方法在新的脑机接口解决方案的背景下的潜在应用。
Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRFenv). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.
登录
查看更多内容
影响因子：
3.4
作者：
Lalor, Edmund C.;Foxe, John J.
通讯作者：
Foxe, John J.
影响因子：
3.8
作者：
Koelsch S
通讯作者：
Koelsch S
影响因子：
4.6
作者：
Sankaran N;Thompson WF;Carlile S;Carlson TA
通讯作者：
Carlson TA
影响因子：
5.3
作者：
Liberto, Giovanni M. Di;Marion, Guilhem;Shamma, Shihab A.
通讯作者：
Shamma, Shihab A.
影响因子：
5.3
作者：
Marion, Guilhem;Liberto, Giovanni M. Di;Shamma, Shihab A.
通讯作者：
Shamma, Shihab A.