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首页> 外文期刊>Neuroinformatics >Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems
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Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

机译：表征振荡eEG回归问题中空间滤波器优化的正则化技术

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We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.
机译：我们报告单试脑态解码的新型监督算法。它们的可靠性和鲁棒性对于有效地在闭环中能够有效地执行神经技术应用。当通过多通道记录评估大脑活动时，由源功率分配（SPOC）算法计算的空间滤波器允许识别振荡子空间。它们回归到已知的连续试验方向变量反射，例如，刺激特征，认知处理或行为。在小型数据集方案中，这种监督方法倾向于通过脑电图（EEG），磁性脑图或局部场电位作为所涉及的录制，往往会过度措施，通常提供低信噪比。为了提高这一点，我们提出并表征了SPOC的三种类型的正则化技术：使用Tikhonov规则化的方法（通过交叉验证需要模型选择），Tikhonov正规化和协方差矩阵标准化的组合以及利用分析协方差矩阵收缩的策略。所有所提出的技术都在新颖的仿真框架和实际数据中进行了评估。根据仿真结果，我们看到我们的期望实现，SPOC正规普遍揭示了小型训练集和严重标签噪声条件下最大的利益。对于从业者而言，我们导出了基于交叉验证的方法的正则化超参数的运行范围，并提供了开源代码。在实际数据上评估所有方法，我们观察了具有最初表现不佳的受试者的数据集的提高回归性能。通过这种概念验证纸，我们为SPOC提供了可宽的正则化框架，其可以作为在未来实施先进技术的起点。

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