脑-机接口
神经形态工程学
计算机科学
可穿戴计算机
脑电图
晶体管
人工神经网络
人工智能
神经科学
嵌入式系统
电气工程
工程类
电压
心理学
作者
Shuangqing Fan,Enhua Wu,Minghui Cao,Ting Xu,Tong Liu,Le Yang,Jie Su,Jing Liu
出处
期刊:Materials horizons
[The Royal Society of Chemistry]
日期:2023-01-01
卷期号:10 (10): 4317-4328
被引量:4
摘要
Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.
科研通智能强力驱动
Strongly Powered by AbleSci AI