High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays.
High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays.
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DOI：
10.3389/fncir.2017.00020
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发表时间：
2017
影响因子：
3.5
通讯作者：
Suzuki T
中科院分区：
文献类型：
--
作者：
Kaiju T;Doi K;Yokota M;Watanabe K;Inoue M;Ando H;Takahashi K;Yoshida F;Hirata M;Suzuki T
皮质电图（ECoG）作为一种源信号具有很大的潜力，尤其是在临床BMI方面。直到最近，ECoG电极通常用于临床情况下识别致痫灶，这种电极低密度和大。增加记录通道的数量和密度可以收集更丰富的运动/感觉信息，并可能提高解码的精度，增加控制外部设备的机会。有几份报告旨在增加渠道的数量和密度。然而，很少有研究讨论高密度脑电图阵列的实际有效性。在这项研究中，我们开发了新型高密度柔性ECoG阵列，并对猴体感诱发电位（SEPs）进行了解码分析。采用MEMS技术制备了96通道的聚对二甲苯电极阵列，电极间距为700 μm，记录点面积为350 μm2。这些阵列主要放置在猕猴体感皮层的手指表征区，部分插入中央沟。通过手指电刺激，我们成功地以高时空分辨率记录和可视化手指sep。我们进行了离线分析，使用支持向量机从记录的sep中预测受刺激的手指和强度。我们获得了以下结果：(1)仅用短段数据（距离刺激开始约15ms）就获得了非常高的准确率（约98%）。(2)即使只使用单通道，也能达到较高的准确度（96%）。这个结果表明了解码的最佳放置。(3)较高的通道数通常会提高预测精度，但对于包含时间序列信息的特征向量的预测效果较小。这些结果表明，具有高时空分辨率的ECoG信号可以实现更高的解码精度或外部设备控制。
Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm2. The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.