Neural oscillations are routinely analyzed using methods that measure activity in fixed frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm ("ORCA"), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands, which incorporates two new methods for frequency band identification. ORCA uses the instantaneous amplitude, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed "resting" conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that allows researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.

中文翻译：

---

振荡重建算法 (ORCA) 自适应地识别频带以改善人类和啮齿动物神经记录中的频谱分解

神经振荡通常使用测量固定频带（例如 alpha，8-12 Hz）活动的方法进行分析，尽管神经信号的频率会根据包括神经解剖学、行为需求和物种在内的众多因素在个体内部和个体之间变化。此外，带限活动是一种经常假设的、通常无法测量的神经活动模型，并且带定义在不同研究中差异很大。这些因素共同掩盖了个体差异，并在将电生理学与行为联系起来时可能导致嘈杂的频谱估计和解释问题。我们开发了振荡重建算法（“ORCA”），这是一种无监督方法，用于测量自适应识别频带中神经信号的频谱特征，它结合了两种新的频带识别方法。ORCA 使用每个波段中活动的瞬时幅度、相位和频率来重建信号，并使用四种不同模型中的每一种直接量化频谱分解性能。为了减少研究人员的偏见，ORCA 提供了从最佳模型得出的谱估计，并且需要最少的超参数化。在睁眼和闭眼“休息”条件下分析人类头皮 EEG 数据，我们首先确定受试者和电极之间神经信号频率内容的变异性。我们证明，与传统方法相比，ORCA 显着改善了频谱分解，并捕获了众所周知的电极和受试者特定频带中眼睛闭合期间低频活动的增加。我们进一步说明了我们的方法在啮齿动物 CA1 记录中的效用。ORCA 是一种新颖的分析工具，可让研究人员研究非平稳神经振荡如何因行为、大脑区域、个体和物种而异。