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Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.

Jianting Shi1, Jiaqi Wang1, Weijie Fei1

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Summary
This summary is machine-generated.

This study introduces a novel brain-computer interface (BCI) for sound target detection (STD), improving robustness in noisy conditions. A hybrid neuro-acoustic system enhances detection accuracy and generalizes to new sound types.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Acoustic Sensing

Background:

  • Automated sound target detection (STD) methods lack robustness and generalization, especially in low signal-to-noise ratio (SNR) environments or with novel sound categories.
  • Existing systems struggle with reliability and accuracy in complex acoustic scenes, limiting their real-world applicability.

Purpose of the Study:

  • To develop a robust and generalizable sound target detection (STD) method by integrating brain-computer interface (BCI) technology with conventional acoustic sensing.
  • To enhance the accuracy and interpretability of STD in complex auditory environments using neural responses.
  • To overcome the limitations of standalone BCI systems, such as high false alarm rates, through a hybrid fusion strategy.

Main Methods:

  • Proposed a Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors from EEG source analysis.
  • Developed an adaptive confidence-based brain-machine fusion strategy to combine BCI and acoustic detection model decisions.
  • Conducted experiments with 16 participants to validate the neuro-acoustic fusion approach.

Main Results:

  • The Tri-SDANet achieved state-of-the-art performance in neural decoding under complex acoustic conditions.
  • The hybrid system demonstrated reliable detection performance at low SNR levels and remarkable generalization to unseen target classes.
  • Source-level EEG analysis revealed distinct brain activation patterns linked to target perception, validating the model's design.

Conclusions:

  • Pioneered a neuro-acoustic fusion paradigm for robust and generalizable sound target detection (STD).
  • The integrated system effectively merges neural perception and acoustic feature learning, offering a significant advancement over existing methods.
  • This approach provides a promising, generalizable solution for real-world acoustic sensing applications by leveraging noninvasive neural signals and artificial intelligence.