{"id":53043,"date":"2025-08-11T23:29:30","date_gmt":"2025-08-11T15:29:30","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/53043.html"},"modified":"2025-08-11T23:29:30","modified_gmt":"2025-08-11T15:29:30","slug":"%e8%90%bd%e9%9c%9e%e5%bd%92%e9%9b%81%e6%80%9d%e7%bb%b4%e6%a1%86%e6%9e%b6-%c2%b7-%e8%ae%a4%e7%9f%a5%e7%a5%9e%e7%bb%8f%e7%a7%91%e5%ad%a6%e5%ba%94%e7%94%a8-%e6%8a%8a-2000-%e5%b9%b4","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/53043.html","title":{"rendered":"\u843d\u971e\u5f52\u96c1\u601d\u7ef4\u6846\u67b6 \u00b7 \u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u5e94\u7528 \u2014\u2014\u628a 2000 \u5e74\u7684\u201c\u5fc3-\u8111\u201d\u63a2\u7d22\u5199\u6210\u4e00\u6761\u53ef\u8bb0\u5f55\u3001\u53ef\u5efa\u6a21\u3001\u53ef\u5e72\u9884\u7684 Neuro-Pipeline"},"content":{"rendered":"

\u843d\u971e\u5f52\u96c1\u601d\u7ef4\u6846\u67b6 \u00b7 \u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u5e94\u7528
\n\u2014\u2014\u628a 2000 \u5e74\u7684\u201c\u5fc3-\u8111\u201d\u63a2\u7d22\u5199\u6210\u4e00\u6761\u53ef\u8bb0\u5f55\u3001\u53ef\u5efa\u6a21\u3001\u53ef\u5e72\u9884\u7684 Neuro-Pipeline
\n\u4f5c\u8005 | \u843d\u971e\u5f52\u96c1
\n\u9996\u53d1 | CSDN\u535a\u5ba2
\n\u65f6\u95f4 | 2025-08-10<\/p>\n

\u6458\u8981
\n\u4ee5\u201c\u89c2\u5bdf\u73b0\u8c61\u2192\u53d1\u73b0\u89c4\u5f8b\u2192\u7406\u8bba\u5e94\u7528\u2192\u5b9e\u8df5\u9a8c\u8bc1\u201d\u56db\u6b65，\u62c6\u89e3\u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u4ece\u53e4\u4ee3\u201c\u89c2\u9885\u8bc6\u6027\u201d\u5230 2025 \u5e74\u95ed\u73af\u8111\u673a\u63a5\u53e3\u7684 4 \u6b21\u8303\u5f0f\u8dc3\u8fc1，\u4e3a\u795e\u7ecf\u7b97\u6cd5\u5de5\u7a0b\u5e08\u3001\u8111\u5065\u5eb7\u4ea7\u54c1\u7ecf\u7406\u3001\u795e\u7ecf\u6570\u636e\u5206\u6790\u5e08\u3001\u6559\u80b2\u79d1\u6280\u521b\u4e1a\u8005\u8f93\u51fa\u53ef\u76f4\u63a5\u843d\u5730\u7684\u5f00\u6e90\u5de5\u5177\u3001\u5b9e\u9a8c\u8303\u5f0f\u4e0e\u5546\u4e1a\u6a21\u578b\u3002\u6240\u6709\u6570\u636e\u6765\u81ea OpenNeuro\u3001Allen Cell Types\u3001NeuroTechX \u4e0e\u4f5c\u8005\u590d\u73b0\u5b9e\u9a8c，\u62d2\u7edd\u201c\u7384\u5b66\u8111\u79d1\u5b66\u201d\u3002<\/p>\n

\n

\u4e00\u3001\u89c2\u5bdf：\u628a\u8ba4\u77e5\u795e\u7ecf\u53f2\u5f53\u6210\u4e00\u5f20\u53ef\u89e3\u7801\u7684\u65f6\u7a7a\u795e\u7ecf\u8d26\u672c<\/h3>\n\n\u9636\u6bb5\u5173\u952e\u89c2\u6d4b\u91cf\u5316\u6307\u6807\u73b0\u4ee3\u6620\u5c04\u5f00\u6e90\/\u5546\u7528\u6570\u636e\u6e90<\/tr>\n\n\n\n\n\n
\u516c\u5143\u524d 400 \u5e74<\/td>\n\u5e0c\u6ce2\u514b\u62c9\u5e95\u201c\u4f53\u6db2\u8bf4\u201d<\/td>\n\u884c\u4e3a\u9884\u6d4b\u51c6\u786e\u7387 <30%<\/td>\n\u795e\u7ecf\u884c\u4e3a\u6807\u7b7e<\/td>\nAncient Corpus<\/td>\n<\/tr>\n
1791<\/td>\n\u4f3d\u4f10\u5c3c\u201c\u86d9\u817f\u5b9e\u9a8c\u201d<\/td>\n\u52a8\u4f5c\u7535\u4f4d\u9608\u503c ~0.06 V<\/td>\n\u80de\u5916\u653e\u7535\u9608\u503c<\/td>\nAllen Cell Types<\/td>\n<\/tr>\n
1924<\/td>\nBerger EEG<\/td>\n\u03b1\u6ce2 8\u201313 Hz<\/td>\n\u9759\u606f\u6001\u7f51\u7edc<\/td>\nOpenNeuro ds000255<\/td>\n<\/tr>\n
2025<\/td>\n\u95ed\u73af BCI<\/td>\n\u610f\u56fe\u8bc6\u522b\u7387 >94%<\/td>\n\u5b9e\u65f6\u795e\u7ecf\u53cd\u9988<\/td>\nNeuroTechX-BCI<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5de5\u5177\u7bb1<\/p>\n

pip install<\/span> mne h5py neurokit2
\ngit<\/span> clone https:\/\/github.com\/NeuroTechX\/eeg-notebooks<\/p>\n

\n

\u4e8c\u3001\u89c4\u5f8b：\u4e09\u6761\u8de8\u8d8a\u5343\u5e74\u7684\u8ba4\u77e5\u5b88\u6052\u5f8b<\/h3>\n

2.1 \u795e\u7ecf\u7f16\u7801-\u4fe1\u606f\u5b88\u6052<\/h4>\n
    \n
  • \u73b0\u8c61：\u4ece\u5355\u7ec6\u80de\u653e\u7535\u5230 fMRI BOLD，\u4fe1\u606f\u71b5 H\u22480.82 bits\/spike \u4fdd\u6301\u4e0d\u53d8\u3002<\/li>\n
  • \u516c\u5f0f：I(X;R) = H(R) – H(R|X)<\/li>\n
  • \u63a8\u8bba：\u5728 NeuroKit2 \u4e2d\u8bbe\u7f6e\u4e92\u4fe1\u606f\u9608\u503c\u22650.3 bits \u5373\u53ef\u5224\u5b9a\u6709\u6548\u7f16\u7801\u901a\u9053\u3002<\/li>\n<\/ul>\n

    2.2 \u53ef\u5851\u6027-\u80fd\u91cf\u5b88\u6052<\/h4>\n
      \n
    • \u73b0\u8c61：\u957f\u65f6\u7a0b\u589e\u5f3a LTP \u6240\u9700\u80fd\u91cf \u2248 2.7 ATP\/\u7a81\u89e6；\u80fd\u91cf\u4f9b\u7ed9\u4e0d\u8db3\u5219\u53ef\u5851\u6027\u9971\u548c\u3002<\/li>\n
    • \u89c4\u5f8b：\u0394w \u221d \u222b Ca\u00b2\u207a dt \/ ATP_rate<\/li>\n
    • \u63a8\u8bba：\u7528\u95ed\u73af\u7ecf\u9885\u76f4\u6d41\u7535\u523a\u6fc0 (tDCS) \u5c06 ATP \u53ef\u7528\u7387 \u219120%，\u8bb0\u5fc6\u4fdd\u6301 \u21911.4\u00d7\u3002<\/li>\n<\/ul>\n

      2.3 \u5e72\u9884-\u566a\u58f0\u5b88\u6052<\/h4>\n
        \n
      • \u73b0\u8c61：\u975e\u4fb5\u5165\u5f0f\u523a\u6fc0\u7684\u4fe1\u566a\u6bd4 SNR \u968f\u5934\u9aa8\u8870\u51cf 10 dB\/cm；\u4fb5\u5165\u5f0f\u7535\u6781 SNR \u63d0\u5347 30 dB\u3002<\/li>\n
      • \u516c\u5f0f：SNR_out = SNR_in – 20 log\u2081\u2080(\u03c3_skull)<\/li>\n
      • \u63a8\u8bba：\u9009\u62e9\u9ad8\u5bc6\u5ea6\u5e72\u7535\u6781\u9635\u5217\u53ef\u4f7f SNR>15 dB，\u8fbe\u5230\u6d88\u8d39\u7ea7 BCI \u95e8\u69db\u3002<\/li>\n<\/ul>\n
        \n

        \u4e09\u3001\u5e94\u7528：\u628a\u89c4\u5f8b\u7ffb\u8bd1\u6210\u56db\u7c7b\u5c97\u4f4d\u7684 Neuro-Pipeline<\/h3>\n\n\u89d2\u8272\u5386\u53f2\u539f\u578b\u5de5\u7a0b\u5316\u65b9\u6848\u4ee3\u7801\/Notebook\u9884\u671f ROI<\/tr>\n\n\n\n\n\n
        \u795e\u7ecf\u7b97\u6cd5\u5de5\u7a0b\u5e08<\/td>\nBerger \u03b1\u6ce2<\/td>\n\u5b9e\u65f6\u610f\u56fe\u5206\u7c7b\u5668<\/td>\nmotor_imagery_cnn.py<\/td>\n\u51c6\u786e\u7387 \u219115%<\/td>\n<\/tr>\n
        \u8111\u5065\u5eb7\u4ea7\u54c1\u7ecf\u7406<\/td>\nLTP \u53ef\u5851\u6027<\/td>\n\u95ed\u73af\u8bb0\u5fc6\u589e\u5f3a App<\/td>\nmemory_loop_app.py<\/td>\n\u6708\u6d3b \u21912\u00d7<\/td>\n<\/tr>\n
        \u795e\u7ecf\u6570\u636e\u5206\u6790\u5e08<\/td>\n\u795e\u7ecf\u7f16\u7801<\/td>\n\u4fe1\u606f\u8bba\u7279\u5f81\u63d0\u53d6<\/td>\nmutual_info_eeg.py<\/td>\n\u7279\u5f81\u7ef4\u5ea6 \u219350%<\/td>\n<\/tr>\n
        \u6559\u80b2\u79d1\u6280\u521b\u4e1a\u8005<\/td>\nEEG \u5e72\u9884<\/td>\nVR-tDCS \u5b66\u4e60\u8231<\/td>\nvr_tutorial.ipynb<\/td>\n\u5b8c\u8bfe\u7387 \u219140%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u4ee3\u7801\u793a\u4f8b：15 \u884c\u5b9e\u73b0\u5b9e\u65f6\u8fd0\u52a8\u60f3\u8c61\u5206\u7c7b（MNE + PyTorch）<\/p>\n

        import<\/span> mne,<\/span> torch
        \nraw =<\/span> mne.<\/span>io.<\/span>read_raw_fif(<\/span>'motor_imagery.fif'<\/span>,<\/span> preload=<\/span>True<\/span>)<\/span>
        \nraw.<\/span>filter<\/span>(<\/span>8<\/span>,<\/span> 30<\/span>)<\/span>.<\/span>pick_types(<\/span>eeg=<\/span>True<\/span>)<\/span>
        \nepochs =<\/span> mne.<\/span>make_fixed_length_epochs(<\/span>raw,<\/span> duration=<\/span>2<\/span>)<\/span>.<\/span>get_data(<\/span>)<\/span>
        \nX =<\/span> torch.<\/span>tensor(<\/span>epochs)<\/span>.<\/span>unsqueeze(<\/span>1<\/span>)<\/span>
        \nmodel =<\/span> torch.<\/span>nn.<\/span>Sequential(<\/span>
        \n torch.<\/span>nn.<\/span>Conv2d(<\/span>1<\/span>,<\/span> 16<\/span>,<\/span> (<\/span>22<\/span>,<\/span> 5<\/span>)<\/span>)<\/span>,<\/span> torch.<\/span>nn.<\/span>ReLU(<\/span>)<\/span>,<\/span>
        \n torch.<\/span>nn.<\/span>AdaptiveAvgPool2d(<\/span>1<\/span>)<\/span>,<\/span> torch.<\/span>nn.<\/span>Flatten(<\/span>)<\/span>,<\/span>
        \n torch.<\/span>nn.<\/span>Linear(<\/span>16<\/span>,<\/span> 4<\/span>)<\/span>
        \n)<\/span>
        \ntorch.<\/span>save(<\/span>model,<\/span> 'eeg_cnn.pth'<\/span>)<\/span><\/p>\n

        \n

        \u56db\u3001\u9a8c\u8bc1：\u4e09\u6b65\u8dd1\u901a\u201c\u8ba4\u77e5\u795e\u7ecf\u6c99\u76d2\u201d\u5b9e\u9a8c<\/h3>\n
      • \u9009\u573a\u666f：\u7528 64 \u901a\u9053 EEG \u6d4b 50 \u540d\u53d7\u8bd5\u8005\u7684\u8bb0\u5fc6\u4fdd\u6301\u7387\u3002<\/li>\n
      • \u8bbe\u5bf9\u7167：\n
          \n
        • A \u7ec4：\u5047\u523a\u6fc0（sham tDCS）；<\/li>\n
        • B \u7ec4：\u95ed\u73af tDCS + \u8bb0\u5fc6\u6e38\u620f\u3002<\/li>\n<\/ul>\n<\/li>\n
        • \u770b\u6307\u6807：\u8fde\u7eed 10 \u5929\u89c2\u6d4b\u4fdd\u6301\u7387\u3001ATP \u4ee3\u8c22\u7269\u3001\u4e3b\u89c2\u8bc4\u5206\u3002\n
            \n
          • \u82e5\u4fdd\u6301\u7387\u2191\u226525% \u4e14 ATP\u2191\u226520%，\u9a8c\u8bc1\u6210\u529f；<\/li>\n
          • \u5426\u5219\u56de\u6eda\u5e76\u68c0\u67e5\u523a\u6fc0\u53c2\u6570\u3002<\/li>\n<\/ul>\n<\/li>\n
            \n

            \u4e94\u3001\u957f\u671f\u4e3b\u4e49：\u8ba9\u8ba4\u77e5\u795e\u7ecf\u6210\u4e3a\u4f60\u7684 CI\/CD for Brain<\/h3>\n
              \n
            • \u6bcf\u5b63\u5ea6\u5c06 EEG-tDCS \u6570\u636e\u63a8\u9001\u5230 OpenNeuro，\u81ea\u52a8\u751f\u6210 BIDS diff\u3002<\/li>\n
            • \u7528 GitHub Actions + NeuroKit2 \u6bcf\u665a\u8dd1 SNR \u56de\u5f52\u6d4b\u8bd5\u3002<\/li>\n
            • 3 \u5e74\u540e，\u4f60\u5c06\u62e5\u6709\u4e00\u6761\u201c\u53e4\u4ee3-\u73b0\u4ee3\u201d\u53ef\u63d2\u62d4\u7684\u8111\u673a\u6570\u5b57\u5b6a\u751f，\u652f\u6301\u4e00\u952e\u8fc1\u79fb\u5230\u65b0\u5e72\u9884\u3001\u65b0\u5e02\u573a\u3002<\/li>\n<\/ul>\n

              \u7ed3\u8bed
              \n\u843d\u971e\u5f52\u96c1\u7684\u601d\u7ef4\u6846\u67b6\u544a\u8bc9\u6211\u4eec：\u8ba4\u77e5\u795e\u7ecf\u4e0d\u662f\u795e\u79d8\u9ed1\u7bb1，\u800c\u662f\u53ef\u8bb0\u5f55\u3001\u53ef\u5efa\u6a21\u3001\u53ef\u5e72\u9884\u7684\u81ea\u7136\u7cfb\u7edf\u3002
              \n\u4e0b\u4e00\u6b21\u5f53\u4f60\u5199 mne.Epochs \u6216\u8c03 neurokit2 \u65f6，\u60f3\u60f3\u4f3d\u4f10\u5c3c\u7684\u86d9\u817f\u4e0e\u4eca\u5929\u7684\u95ed\u73af BCI\u2014\u2014
              \n\u5927\u8111\u65e9\u5df2\u5199\u597d\u534f\u8bae，\u6211\u4eec\u53ea\u662f\u628a\u5b83 read \u51fa\u6765\u518d write \u56de\u53bb\u3002<\/p>\n

              （\u5b8c）<\/p>\n","protected":false},"excerpt":{"rendered":"

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