Neural interface technology for human-computer interaction

Abstract

Brain-Computer Interfaces (BCI) offer a direct communication channel between the brain and external devices, marking a pivotal convergence of neuroscience and technology. Neural activities, essentially electrical impulses, can be captured either invasively, with methods such as Electrocorticography (ECoG) which require surgical implantation, or non-invasively using techniques like Electroencephalography (EEG) that operate externally. Once acquired, raw neural data undergoes processing; external and physiological noises are filtered out, and meaningful patterns or neural fingerprints are extracted. Modern BCIs then employ machine learning, specifically deep learning, to translate these cleaned neural patterns into discernible commands, with continuous feedback loops enhancing system adaptability. These decoded signals can control varied devices, from medical-grade robotic limbs to cursors on screens. BCIs have transformative applications across sectors: theyre pivotal in neurorehabilitation after brain injuries, providing feedback where traditional methods might fail; theyre integrated with virtual reality for immersive feedback; they revolutionize cognitive training and meditation practices; and theyre finding a foothold in high-risk sectors like deep-sea exploration and military operations. Additionally, research on tactile Event-Related Potential (ERP)-based BCIs emphasizes the importance of congruent Control-Display Mapping for efficient user experience. However, with these advancements come ethical concerns, such as the potential invasion of the privacy of ones thoughts, challenges to human identity and autonomy, societal disparities in access, and health implications.