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Peer-reviewedCC BY-NC 4.0Science Advances

A high robustness decoding technique for ultrasound modulated electroencephalogram

  • Xue Wang1
  • Guowei Chen1
  • Jia He1
  • Pu Wang1
  • Rongxu Guo1
  • Shenjie Ji1
  • Mingyu Li1
  • Feng He1,2
  • Yanqiu Zhang3
  • Xiqi Jian3
  • Hao Zhang1,2
  • Minpeng Xu1,2
1 more authors
  • Dong Ming1,2
  1. 1Academy of Medical Engineering and Translational Medicine, State Key Laboratory of Advanced Medical Materials and Devices, and Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China.
  2. 2Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China.
  3. 3School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
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Abstract

Ultrasound-modulated electroencephalography (USMEEG) has become a vital tool in neuroscience because it integrates millimeter-level spatial resolution with millisecond-level temporal resolution. However, under extremely complex scenarios, excessively strong or weak acoustoelectric signal coupling leads to severe distortion in USMEEG decoded signals. To address this, we developed a highly robust decoding algorithm based on the ultrasound pulse repetition frequency and introduced the acoustoelectric modulation ratio (AMR) to quantify different modulation scenarios. Across numerical simulations, phantoms, and validation using human steady-state visual evoked potential signals with data-level acoustoelectric coupling across 10 volunteers, the focused ultrasound phased array achieved an encoding spatial resolution of 0.79 millimeters. The proposed algorithm significantly outperformed four traditional methods, exhibiting higher precision under weak modulation (0 < AMR ≤ 0.6), moderate modulation (0.6 < AMR ≤ 1), and over modulation (AMR > 1) conditions, with average decoding correlations reaching 0.84, 0.90, and 0.89. This highly robust technique expands USMEEG applications and provides a technical foundation for high-performance noninvasive brain-computer interfaces.

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Cite

BibTeXRIS
BibTeX
@article{wang2026high,
  title = {{A high robustness decoding technique for ultrasound modulated electroencephalogram}},
  author = {Xue Wang and Guowei Chen and Jia He and Pu Wang and Rongxu Guo and Shenjie Ji and Mingyu Li and Feng He and Yanqiu Zhang and Xiqi Jian and Hao Zhang and Minpeng Xu and Dong Ming},
  journal = {Science Advances},
  year = {2026},
  month = nov,
  doi = {10.1126/sciadv.aei1954},
  url = {https://www.science.org/doi/abs/10.1126/sciadv.aei1954},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - A high robustness decoding technique for ultrasound modulated electroencephalogram
AU  - Xue Wang
AU  - Guowei Chen
AU  - Jia He
AU  - Pu Wang
AU  - Rongxu Guo
AU  - Shenjie Ji
AU  - Mingyu Li
AU  - Feng He
AU  - Yanqiu Zhang
AU  - Xiqi Jian
AU  - Hao Zhang
AU  - Minpeng Xu
AU  - Dong Ming
PY  - 2026
DA  - 2026/11/04
JO  - Science Advances
DO  - 10.1126/sciadv.aei1954
UR  - https://www.science.org/doi/abs/10.1126/sciadv.aei1954
AB  - Ultrasound-modulated electroencephalography (USMEEG) has become a vital tool in neuroscience because it integrates millimeter-level spatial resolution with millisecond-level temporal resolution. However, under extremely complex scenarios, excessively strong or weak acoustoelectric signal coupling leads to severe distortion in USMEEG decoded signals. To address this, we developed a highly robust decoding algorithm based on the ultrasound pulse repetition frequency and introduced the acoustoelectric modulation ratio (AMR) to quantify different modulation scenarios. Across numerical simulations, phantoms, and validation using human steady-state visual evoked potential signals with data-level acoustoelectric coupling across 10 volunteers, the focused ultrasound phased array achieved an encoding spatial resolution of 0.79 millimeters. The proposed algorithm significantly outperformed four traditional methods, exhibiting higher precision under weak modulation (0 < AMR ≤ 0.6), moderate modulation (0.6 < AMR ≤ 1), and over modulation (AMR > 1) conditions, with average decoding correlations reaching 0.84, 0.90, and 0.89. This highly robust technique expands USMEEG applications and provides a technical foundation for high-performance noninvasive brain-computer interfaces.
SN  - 2375-2548
ER  -