Rodriguez Fernando, He Shenghong, Tan Huiling
MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Front Hum Neurosci. 2023 Jun 2;17:1134599. doi: 10.3389/fnhum.2023.1134599. eCollection 2023.
Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems.
对于适应性深部脑刺激（aDBS）和其他脑机接口（BCI）应用而言，实时处理传入的神经振荡信号并从中解码相关的行为或病理状态通常是必要的。当前大多数方法首先依赖于提取一组预定义特征，例如标准频段的功率或各种时域特征，然后训练机器学习系统，这些系统将这些预定义特征作为输入，并推断每个给定时间点的潜在脑状态。然而，这种算法方法是否最适合提取神经波形中包含的所有可用信息仍然是一个悬而未决的问题。在这里，我们旨在探索不同的算法方法，根据基于神经活动（如通过局部场电位（LFP）记录或脑电图（EEG）测量）在解码性能方面产生改进的潜力。特别是，我们旨在探索端到端卷积神经网络的潜力，并将这种方法与基于提取预定义特征集的其他机器学习方法进行比较。为此，我们实现并训练了一些机器学习模型，这些模型要么基于手动构建的特征，要么在基于深度学习的模型中，基于直接从数据中学到的特征。我们使用模拟数据对这些模型在识别神经状态的任务上进行基准测试，模拟数据包含先前与生理和病理功能相关的波形特征。然后，我们根据从特发性震颤患者的运动丘脑记录的局部场电位评估这些模型在解码运动方面的性能。我们从模拟和真实患者数据中得出的结果表明，基于端到端深度学习的方法可能优于基于特征的方法，特别是当波形数据中的相关模式未知、难以量化时，或者从预定义特征提取管道的角度来看，可能存在有助于解码性能的未识别特征时。本研究中提出的方法可能在适应性深部脑刺激（aDBS）和其他脑机接口系统中具有应用潜力。
