Martineau Thomas, He Shenghong, Vaidyanathan Ravi, Tan Huiling
Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom.
Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.
Front Hum Neurosci. 2023 May 24;17:1111590. doi: 10.3389/fnhum.2023.1111590. eCollection 2023.
Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience.
This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients.
Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models.
Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.
从皮层下局部场电位（LFP）中解码大脑状态，这些电位指示诸如自主运动、震颤或睡眠阶段等活动，在治疗神经退行性疾病方面具有巨大潜力，并为脑机接口（BCI）提供了新的范例。识别出的状态可作为耦合人机系统中的控制信号，例如用于调节深部脑刺激（DBS）治疗或控制假肢。然而，LFP解码器的行为、性能和效率取决于封装在一组超参数中的一系列设计和校准设置。尽管存在自动调整超参数的方法，但解码器通常是通过详尽的试错、手动搜索和直观经验找到的。
本研究引入了一种贝叶斯优化（BO）方法用于超参数调整，该方法适用于整个解码流程的特征提取、通道选择、分类和阶段转换阶段。将该优化方法与五种实时特征提取方法以及四种分类器进行比较，以基于植入帕金森病患者丘脑底核的DBS电极记录的LFP异步解码自主运动。
以分类器特异性和敏感性之间的几何平均值衡量的检测性能得到自动优化。BO在所有方法中均展示了从初始参数设置开始的解码性能提升。最佳解码器实现了0.74±0.06（所有参与者的平均值±标准差）的敏感性-特异性几何平均值的最大性能。此外，使用BO代理模型确定参数相关性。
超参数往往在不同用户之间次优地固定，而不是针对解码任务进行单独调整甚至专门设置。随着解码问题的演变，每个参数与优化问题的相关性以及算法之间的比较也可能难以追踪。我们认为，所提出的解码流程和BO方法是解决围绕超参数调整的此类挑战的一个有前途的解决方案，并且该研究的结果可为未来用于自适应DBS和BCI的神经解码器的设计迭代提供参考。
