Implantable brain–computer interface (BCI) devices are an effective tool to decipher fundamental brain mechanisms and treat neural diseases. However, traditional neural implants with rigid or bulky cross-sections cause trauma and decrease the quality of the neuronal signal. Here, we propose a MEMS-fabricated flexible interface device for BCI applications. The microdevice with a thin film substrate can be readily reduced to submicron scale for low-invasive implantation. An elaborate silicon shuttle with an improved structure is designed to reliably implant the flexible device into brain tissue. The flexible substrate is temporarily bonded to the silicon shuttle by polyethylene glycol. On the flexible substrate, eight electrodes with different diameters are distributed evenly for local field potential and neural spike recording, both of which are modified by Pt-black to enhance the charge storage capacity and reduce the impedance. The mechanical and electrochemical characteristics of this interface were investigated in vitro. In vivo, the small cross-section of the device promises reduced trauma, and the neuronal signals can still be recorded one month after implantation, demonstrating the promise of this kind of flexible BCI device as a low-invasive tool for brain–computer communication.
HfOxNy thin films were deposited on polished and oxidized silicon wafers at different nitrogen-oxygen gas flow rates by DC magnetron reactive sputtering, and temperature sensors based on these HfOxNy thin films were fabricated using a microelectromechanical system micromachining process. The resistance–temperature dependencies of these sensors were studied in the temperature range of 4.2 K–300 K, and the effect of the sputtering gas flow rate on the initial resistivity and sensitivity [temperature coefficient of resistance and absolute sensitivity (Sa)] was discussed. One of these sensors was subjected to 15 cycles between 300 K and 4.2 K for thermal cycle stability testing. The performances of these sensors were compared to the now available negative temperature coefficient thin film temperature sensors (ZrNx, CrNx, RuO2, and ZrOxNy), and they show very outstanding sensitivity and thermal cycle stability. Furthermore, the conduction mechanism of HfOxNy thin films in the cryogenic region was studied for the first time.
Objective. A flexible high-density surface electromyography (HD-sEMG) sensor combined with an adaptive algorithm was used to collect and analyze the swallowing activities of patients with post-stroke PSD. Approach. The electrode frame, modified electrode, and bonded substrate of the sensor were fabricated using a flexible printed circuit process, controlled drop coating, and molding, respectively. The adaptation algorithm was achieved by using Laplace and Teager-Kaiser energy operators to extract active segments, a cross-correlation coefficient matrix (CCCM) to evaluate synergy, and multi-frame real-time dynamic root mean square (RMS) to visualize spatiotemporal information to screen lesions. and level of dysphagia. Finally, support vector machines (SVM) were adopted to explore the classification accuracy of sex, age, and lesion location with small sample sizes. Main results. The sensor not only has a basic low contact impedance (0.262 kΩ) and high signal-to-noise ratio (37.284±1.088 dB) but also achieves other characteristics suitable for clinical applications, such as flexibility (747.67 kPa) and durability (1000 times) balance, simple operation (including initial, repeated, and replacement use), and low cost ($ 15.2). The three conclusions are as follows. CCCM can be used as a criterion for judging the unbalanced muscle region of the patient's neck and can accurately locate unbalanced muscles. The RMS cloud map provides the time consumption, swallowing times, and unbalanced areas. When the lesion location involves the left and right hemispheres simultaneously, it can be used as an evidence of relatively severely unbalanced areas. The classification accuracy of SVM in terms of sex, age, and lesion location was as high as 100%. Significance. The HD-sEMG sensor in this study and the adaptation algorithm will contribute to the establishment of a larger-scale database in the future to establish more detailed and accurate quantitative standards, which will be the basis for developing more optimized screening mechanisms and rehabilitation assessment methods.
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