Neural Enhancement Based on Brain-Computer Interface: Application Progress in Improving Cognitive Abilities

Abstract

Brain-computer interface (BCI) technology is an emerging technology that enables direct interaction between the brain and external devices by recording and decoding neural signals. With the development of neuroscience and machine learning, the application of BCI has expanded to the fields of cognitive enhancement and neurorehabilitation. This study explores the potential of BCI technology in improving learning ability, enhancing memory and promoting neurorehabilitation. BCI relies on neuroplasticity, especially Hebb plasticity and operant conditioning, in which long-term potentiation (LTP) promotes memory and learning ability by repeatedly activating neuronal connections. In addition, neurofeedback technology can help users self-regulate brain activity to optimize cognitive function. In addition, progress has been made in the application of BCI in neurological diseases, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Studies have shown that tDCS helps improve the memory recall ability of AD patients, while EEG combined with games and slow cortical potential (SCP) training can help ADHD patients regulate brain electrical activity and improve attention and impulse control. Although BCI still faces challenges such as signal accuracy and personalized program development, future research can further explore ways to improve signal recognition accuracy and provide more effective solutions for cognitive enhancement and neurorehabilitation.