In the growing field of brain-machine interface (BMI), the interface between electrodes and neural tissues plays an important role in the recording and stimulation of neural signals. To minimize tissue damage while retaining high sensitivity, a flexible and a smaller electrode with low impedance is required. However, it is a major challenge to reduce electrode size while retaining the conductive characteristics of the electrode. In addition, the mechanical mismatch between stiff electrodes and soft tissues creates damaging reactive tissue responses. Here, we demonstrate a neural probe structure based on graphene, ZnO nanowires, and conducting polymer that provides flexibility and low impedance performance. A hybrid Au and graphene structure was utilized to achieve both flexibility and good conductivity. Using ZnO nanowires to increase the effective surface area drastically decreased the impedance value and enhanced the signal-to-noise ratio (SNR). A poly[3,4-ethylenedioxythiophene] (PEDOT) coating on the neural probe improved the electrical characteristics of the electrode while providing better biocompatibility. In vivo neural signal recordings showed that our neural probe can detect clearer signals.

To produce artificial psychological feeling, especially ‘pain’, an electrical structure that mimics human skin has been studied.

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s−1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

No abstract

Tactile sensors mimicking the human sense of touch have been studied and various technologies for the sensing of external stimuli have been suggested as well. Humans detect certain external stimuli and become aware of related sensations, such as roughness or smoothness. Among the various physical parameters, surface information is the most informative type of perception to impart these sensations onto an electromechanical system, such as an android robot or a smart phone. Here, an array sensor, which uses a sliding method for the precise perception of surface information, such as shapes and structures, is demonstrated. The suggested array sensor design with the excellent dynamic response of a piezoelectric material results in enhanced spatial resolutions with sliding motions and detects variable sliding speeds. A soft material was employed to the sensor to enhance the capability of shape distinction. Color mapping was applied to translate surface patterns into visual images. The reconfigured surface information had high accuracy compared to actual information. The demonstrated sliding speed, pattern detection, and shape detection capabilities as well as the higher spatial resolutions allow the sensor to be utilized as an artificial tactile sensor.