Deep brain-machine interfaces: sensing and modulating the human deep brain

Degradable and Recyclable Hydrogels for Sustainable Bioelectronics

Hydrogel bioelectronics has been widely used in wearable sensors, electronic skin, human-machine interfaces, and implantable tissue-electrode interfaces, providing great convenience for human health, safety, and education. The generation of electronic waste from bioelectronic devices jeopardizes human health and the natural environment. The development of degradable and recyclable hydrogels is recognized as a paradigm for realizing the next generation of environmentally friendly and sustainable bioelectronics. This review first summarizes the wide range of applications for bioelectronics, including wearable and implantable devices. Then, the employment of natural and synthetic polymers in hydrogel bioelectronics is discussed in terms of degradability and recyclability. Finally, this work provides constructive thoughts and perspectives on the current challenges toward hydrogel bioelectronics, providing valuable insights and guidance for the future evolution of sustainable hydrogel bioelectronics.

Brain-computer interface digital prescription for neurological disorders

Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.