Takafumi Suzuki

There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo.

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.

Simultaneous recording of neural activity at large scale, in the long-term, and with bio-safety can provide essential data. These data can be used to advance technology for brain–machine interfaces in clinical application, and to understand brain function. Therefore, we present a new multichannel neural recording system that can record up to 4096-channel (ch) electrocorticogram data by multiple connections of customized application-specific integrated circuits (ASICs). Recorded data sampled at a rate of 1kS/s are multiplexed with time division via an integrated multiplex board, and in total 51.2 Mbps raw data for 4096 ch are generated. This system has an ultra wideband (UWB) wireless unit for transmitting the recorded neural signals. From preliminary experiments with a human body-equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps for distances below 20 mm.

ブレイン・マシン・インターフェース(BMI)は脳信号から運動意図・内容を読み取って外部機器を制御する技術である.われわれは脳表電極を用いたBMIにより,筋萎縮性側索硬化症等の重症身体障害者に対する機能再建を目指して研究開発を行っている.これまでにγ帯域活動を用いた連続的な解読制御手法により,ロボットアームのリアルタイム制御システムを開発し,脳表電極留置患者による物体の把握・把握解除に成功した.感染リスク回避のためにはワイヤレス体内埋込化が必須であり,ワイヤレス埋込装置のプロトタイプを開発した.今後は,重症の筋萎縮性側索硬化症を対象として,有線・ワイヤレス埋込の2段階での臨床試験を経て実用化を目指す.