Fructus Xanthii-Inspired Low Dynamic Noise Dry Bioelectrodes for Surface Monitoring of ECG

The microstructured surfaces of bioelectrical dry electrodes are important aspects of dry electrode design. However, traditional surfaces for microstructured bioelectrical dry electrodes are costly to produce and require complex fabrication methods. In this study, a novel stacked-template method is proposed for the first time, rapidly producing microstructured dry electrodes at a low cost and with a large surface area. Three types of microstructured Ag/AgCl thermoplastic polyurethane (TPU) electrodes with a Fructus xanthii-inspired barb structure (FXbs) are prepared using this method; then, the dynamic friction, hair interference resistance, electrochemical, and electrocardiogram (ECG) signal acquisition performance of the electrodes are tested, and the dynamic noise characteristics of the electrodes are comprehensively evaluated with simulated instruments. Compared to the plate structure, the dynamic friction coefficient of the FXbs electrode improved by about 38.8%, exhibiting strong hair interference resistance. In addition, the FXbs electrode exhibits low dynamic noise and comparable performance to the wet electrode, in terms of signal acquisition, when it is tested using simulated instruments. Therefore, the prepared FXbs electrode increases the friction coefficient between the electrode and the skin, which effectively resolves issues related to dynamic noise in bioelectrical signals, making it suitable for dynamic measurements.

Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review

ABSTRACT

Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.

Portable brain-computer interface based on novel convolutional neural network

Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear uncomfortable EEG hats. These procedures usually create a challenge for subjects. In the present study, we propose a portable EEG signal acquisition system. This study consists of two main parts: 1) A novel, portable dry-electrode and wireless brain-computer interface is designed. The EEG signal acquisition board is based on 24 bit, analog-to-digital converters chip and wireless microprocessor unit. The wireless portable brain computer interface device acquires an EEG signal comfortably, and the EEG signals are transmitted to a personal computer via Bluetooth. 2) A convolutional neural network (CNN) classification algorithm is implemented to classify the motor imagery (MI) experiment using novel feature 3-dimension input. The time dimension was reshaped to represent the first and second dimension, and the frequency band was used as the third dimension. Specifically, frequency domain representations of EEG signals obtained via wavelet package decomposition (WPD) are obtained to train CNN. The classification performance in terms of the value of kappa is 0.564 for the proposed method. The t-test results show that the performance improvement of CNN over other selected state-of-the-art methods is statistically significant. Our results show that the proposed design is reliable in measuring EEG signals, and the 3D CNN provides better classification performance than other method for MI experiments.