Scholarlink

<p>This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we demonstrate that each visual stimulus - rectangle, triangle, mixed shapes, or blank screen, arouses a detectably distinct speckle pattern. Our optimized DNN classifier achieved near perfect recall (98%) for rectangles and high recall (91%) for triangles in single‑shape trials and sustained robust performance (82% recall) when multiple shapes appeared simultaneously. Circular stimuli produced subtler and less reliable speckle dynamics and were not classified with consistent accuracy. By leveraging low‑cost optics and scalable AI processing, this technique paves the way for real‑time, portable monitoring of visual cortex activity, offering transformative potential for cognitive neuroscience, brain-machine interfaces, and clinical assessment of visual processing. Future work will expand stimulus complexity, optimize model architectures, and explore multimodal neurophotonic applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Visual cortex speckle imaging for shape recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

查看全文
  • Zeev Kalyuzhner,
  • Sergey Agdarov,
  • Yafim Beiderman,
  • Yevgeny Beiderman,
  • Zeev Zalevsky

摘要

This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we demonstrate that each visual stimulus - rectangle, triangle, mixed shapes, or blank screen, arouses a detectably distinct speckle pattern. Our optimized DNN classifier achieved near perfect recall (98%) for rectangles and high recall (91%) for triangles in single‑shape trials and sustained robust performance (82% recall) when multiple shapes appeared simultaneously. Circular stimuli produced subtler and less reliable speckle dynamics and were not classified with consistent accuracy. By leveraging low‑cost optics and scalable AI processing, this technique paves the way for real‑time, portable monitoring of visual cortex activity, offering transformative potential for cognitive neuroscience, brain-machine interfaces, and clinical assessment of visual processing. Future work will expand stimulus complexity, optimize model architectures, and explore multimodal neurophotonic applications.