Kaiju Taro, Doi Keiichi, Yokota Masashi, Watanabe Kei, Inoue Masato, Ando Hiroshi, Takahashi Kazutaka, Yoshida Fumiaki, Hirata Masayuki, Suzuki Takafumi
Graduate School of Frontier Biosciences, Osaka UniversityOsaka, Japan.
Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka UniversityOsaka, Japan.
Front Neural Circuits. 2017 Apr 11;11:20. doi: 10.3389/fncir.2017.00020. eCollection 2017.
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 μm. 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.
脑皮层电图（ECoG）作为一种源信号具有巨大潜力，尤其对于临床脑机接口而言。直到最近，ECoG电极在临床场景中通常用于识别致痫灶，此类电极密度低且尺寸大。增加记录通道的数量和密度能够收集更丰富的运动/感觉信息，并可能提高解码精度以及增加控制外部设备的机会。已有若干报告旨在增加通道的数量和密度。然而，很少有研究讨论高密度ECoG阵列的实际有效性。在本研究中，我们开发了新型高密度柔性ECoG阵列，并利用猴子体感诱发电位（SEP）进行了解码分析。我们采用微机电系统（MEMS）技术制作了96通道的聚对二甲苯电极阵列，电极间距为700μm，记录位点面积为350μm。这些阵列主要放置在猕猴体感皮层的手指代表区，并部分插入中央沟。通过电刺激手指，我们成功以高时空分辨率记录并可视化了手指SEP。我们进行了离线分析，使用支持向量机从记录的SEP预测受刺激的手指及其强度。我们得到了以下结果：（1）仅使用一小段数据（刺激开始后约15毫秒）就能实现非常高的准确率（约98%）。（2）即使仅使用单个通道也能实现较高的准确率（约96%）。这一结果表明解码的放置最优性。（3）通道数增加通常会提高预测准确率，但对于包含时间序列信息的特征向量进行预测时，效果提升较小。这些结果表明，具有高时空分辨率的ECoG信号能够实现更高的解码精度或外部设备控制。
