With the recent developments in neuroscience and engineering, it is now possible to record brain signals and decode them. Also, a growing number of stimulation methods have emerged to modulate and influence brain activity. Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes, it already demonstrated its efficiency as assistive and rehabilitative technology for patients with severe motor impairments. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been used to decode brain signals. Beyond this progress, combining AI with advanced BCIs in the form of implantable neurotechnologies grants new possibilities for the diagnosis, prediction, and treatment of neurological and psychiatric disorders. In this context, we envision the development of closed loop, intelligent, low-power, and miniaturized neural interfaces that will use brain inspired AI techniques with neuromorphic hardware to process the data from the brain. This will be referred to as Brain Inspired Brain Computer Interfaces (BI-BCIs). Such neural interfaces would offer access to deeper brain regions and better understanding for brain's functions and working mechanism, which improves BCIs operative stability and system's efficiency. On one hand, brain inspired AI algorithms represented by spiking neural networks (SNNs) would be used to interpret the multimodal neural signals in the BCI system. On the other hand, due to the ability of SNNs to capture rich dynamics of biological neurons and to represent and integrate different information dimensions such as time, frequency, and phase, it would be used to model and encode complex information processing in the brain and to provide feedback to the users. This paper provides an overview of the different methods to interface with the brain, presents future applications and discusses the merger of AI and BCIs.
翻译:随着神经科学和工程的近期发展,现在有可能记录大脑信号并解码这些信号。此外,越来越多的刺激方法已经出现,以调节和影响大脑活动。当前的大脑-计算机界面技术(BCI)主要是在治疗结果上,已经证明了它作为严重运动损伤患者的辅助和康复技术的效率。最近,人工智能(AI)和机器学习(ML)技术被用于解码大脑信号。除了这一进展外,将AI与先进的可移植频率神经技术形式的高级BCI结合,为神经和精神病的诊断、预测和治疗提供了新的机会。在这方面,我们设想开发闭路、智能、低功率和微型神经界面(BCI)技术,这些技术将使用神经畸形硬件处理大脑智能和康复技术处理大脑数据。 人工智能智能智能智能(AI)和机器学习(MLM)技术被用来解码。这种神经界面将为大脑更深层的大脑区域提供访问,并为大脑的功能和工作机制提供更好的理解,从而改进BCI的操作稳定性和神经神经障碍的界面的界面和神经系统功能。在SNIS系统中,将使用不同的内部系统,将使用不同的自动算算法和神经系统,将用来解释。