Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution
High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.
Authors
- Dong Ming (ORCID: https://orcid.org/0000-0002-8192-2538)
- Yanqiu Zhang (ORCID: https://orcid.org/0000-0003-1807-2152)
- Hao Zhang (ORCID: https://orcid.org/0000-0002-7377-0589)
- Minpeng Xu (ORCID: https://orcid.org/0000-0001-6746-4828)
- Guowei Chen (ORCID: https://orcid.org/0000-0002-3093-9866)
- Xue Wang (ORCID: https://orcid.org/0009-0009-0299-9675)
- Feng He (ORCID: https://orcid.org/0000-0002-0798-1820)
- Xiqi Jian
Institutions
- Tianjin University (CN)
- Tianjin International Joint Academy of Biomedicine (CN)
- Human Computer Interaction (Switzerland) (CH)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Cyborg and Bionic Systems
- Published
- 2025-01-01
- DOI
- https://doi.org/10.34133/cbsystems.0206
- Citations
- 2
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 1.68