Human-centred physical neuromorphics with visual brain-computer interfaces

• Published in: Nature communications 2024-07, Vol.15 (1), p.6393-8, Article 6393
• Main Authors: Wang, Gao, Marcucci, Giulia, Peters, Benjamin, Braidotti, Maria Chiara, Muckli, Lars, Faccio, Daniele
• Format: Article
• Language: English
• Subjects: 631/378/2613/2615; 639/624/1107/510; 639/705/258; Adult; Brain; Brain - physiology; Brain-Computer Interfaces; Classification; Cognitive tasks; EEG; Electroencephalography; Evoked potentials; Evoked Potentials, Visual - physiology; Female; Frequency division multiplexing; High density; Human-computer interface; Humanities and Social Sciences; Humans; Information processing; Interfaces; Male; Man-machine interfaces; multidisciplinary; Neural networks; Neural Networks, Computer; Photic Stimulation; Science; Science (multidisciplinary); Sensory stimulation; Steady state; Visual evoked potentials; Young Adult
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-50775-2

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions. Steady state visually evoked potentials enable EEG vision-based brain computer interfaces. We show that it is possible to encode information distributed across 100+ frequencies. We encode images and perform simple physical computing classification tasks. Connecting more than one in brain in series improves the classification capability.