Objective. Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area’s localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid. Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimotor cortex. Neurosurgeons blinded to the electrophysiology identified the sensory and motor gyri using neuronavigation based on sulcal anatomy. We quantified the most discriminatory time points in SSEPs temporal profile between the primary motor (M1) and somatosensory (S1) cortex using the Fisher discrimination criterion. We visualized the amplitude gradient of the SSEP over a 2D heat map to provide visual feedback for the delineation of the CS based on electrophysiology. Subsequently, we employed spectral clustering using the entire the SSEP waveform without selecting any time points and grouped ECoG channels in an unsupervised fashion. Main results. Consistently in all patients, two different time points provided almost equal discrimination between anterior and posterior channels, which vividly outlined the CS when we viewed the SSEP amplitude distribution as a spatial 2D heat map. The first discriminative time point was in proximity to the conventionally favored ∼20 ms peak (N20), and the second time point was slightly later than the markedly high ∼30 ms peak (P30). Still, the location of these time points varied noticeably across subjects. Unsupervised clustering approach separated the anterior and posterior channels with an accuracy of 96.3% based on the time derivative of the SSEP trace without the need for a subject-specific time point selection. In contrast, the raw trace resulted in an accuracy of 88.0%. Significance. We show that the unsupervised clustering of the SSEP trace assessed with subdural electrode grids can delineate the CS automatically with high precision, and the constructed heat maps can localize the motor cortex. We anticipate that the spatiotemporal patterns of SSEP fused with machine learning can serve as a useful tool to assist in surgical planning.

客观的。用皮层电图 (ECoG) 记录的体感诱发电位 (SSEP) 用于中央沟 (CS) 识别是常规术中神经生理监测中被广泛接受的程序。临床实践测试短延迟 SSEP 在条形电极上的相位反转。然而，由于手部区域的局部性质，基于波形形态的评估容易受到解释的变化的影响，并且通常需要多个电极放置或电极重新定位。我们通过使用 ECoG 网格捕获的 SSEP 的时空模式研究了无监督描绘 CS 的可行性。方法. 术中，使用放置在感觉运动皮层上的 ECoG 网格记录了 8 名患者的 SSEP。对电生理学不了解的神经外科医生使用基于脑沟解剖结构的神经导航来识别感觉和运动回。我们使用 Fisher 鉴别标准量化了初级运动 (M1) 和体感 (S1) 皮层之间 SSEP 时间分布中最具鉴别力的时间点。我们在 2D 热图上可视化了 SSEP 的振幅梯度，为基于电生理学的 CS 的描绘提供视觉反馈。随后，我们使用整个 SSEP 波形采用频谱聚类，而不选择任何时间点，并以无监督的方式分组 ECoG 通道。主要结果。与所有患者一样，两个不同的时间点在前后通道之间提供了几乎相等的区分，当我们将 SSEP 振幅分布视为空间 2D 热图时，这生动地勾勒出 CS。第一个判别时间点接近传统上受欢迎的 ~20 ms 峰值 (N20)，第二个时间点稍晚于显着高的 ~30 ms 峰值 (P30)。尽管如此，这些时间点的位置在不同受试者之间仍有显着差异。无监督聚类方法基于 SSEP 轨迹的时间导数以 96.3% 的准确度分离前通道和后通道，无需选择特定于受试者的时间点。相比之下，原始轨迹的准确度为 88.0%。意义. 我们表明，用硬膜下电极网格评估的 SSEP 轨迹的无监督聚类可以高精度自动描绘 CS，并且构建的热图可以定位运动皮层。我们预计 SSEP 与机器学习融合的时空模式可以作为辅助手术计划的有用工具。