Neuralite: Enabling Wireless High-Resolution Brain-Computer Interfaces
Intracortical brain-computer interfaces (iBCIs) promise to sense brain activity at an unprecedented scale and resolution. However, unlocking this potential for practical, untethered applications remains an unsolved challenge. The major barrier is the significant wireless bandwidth required to stream high-resolution brain signals. Existing approaches rely on extensive on-device processing, which is severely constrained by the limited resources of iBCI devices, the complexity of brain signals, and the dynamic nature of neural activity. This paper introduces Neuralite, a wireless iBCI system that integrates high-fidelity brain signal models and effective brain sensing mechanisms within an efficient server-driven streaming framework. By thoroughly characterizing brain signal variability, Neuralite adaptively optimizes streaming under dynamic neural conditions, minimizing bandwidth consumption without imposing excessive burdens on resource-constrained iBCI devices. Experimental results demonstrate that Neuralite significantly reduces bandwidth consumption while preserving neural decoding precision across key iBCI components and representative applications.
Authors
- Yuguang Fang (ORCID: https://orcid.org/0000-0002-1079-3871)
- Hongyao Liu (ORCID: https://orcid.org/0009-0002-2323-5913)
- Jun Huang (ORCID: https://orcid.org/0000-0002-6862-4122)
- Junyi Wang (ORCID: https://orcid.org/0000-0001-8929-7522)
- Lan Zhai (ORCID: https://orcid.org/0000-0002-7690-4853)
Institutions
- City University of Hong Kong (HK)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1145/3636534.3690673
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.03
Funders
- Research Grants Council, University Grants Committee
- Hong Kong Government