2026脑机接口：当“意念控制”开始走进手术室

"""
bci_route_radar.py
用雷达图对比2026年脑机接口四大技术路线的成熟度
"""
import numpy as np
import matplotlib.pyplot as plt

ROUTES = {
    "非侵入式":   {"signal_quality": 0.35, "safety": 0.95, "commercial_readiness": 0.85, "bandwidth": 0.20, "regulatory_ease": 0.90},
    "半侵入式":   {"signal_quality": 0.70, "safety": 0.80, "commercial_readiness": 0.65, "bandwidth": 0.55, "regulatory_ease": 0.70},
    "全侵入式":   {"signal_quality": 0.95, "safety": 0.45, "commercial_readiness": 0.40, "bandwidth": 0.90, "regulatory_ease": 0.35},
    "脑脊接口":   {"signal_quality": 0.75, "safety": 0.60, "commercial_readiness": 0.30, "bandwidth": 0.60, "regulatory_ease": 0.40},
}

def radar_plot(data):
    labels = list(next(iter(data.values())).keys())
    n = len(labels)
    angles = np.linspace(0, 2 * np.pi, n, endpoint=False).tolist()
    angles += angles[:1]

    fig, ax = plt.subplots(figsize=(7, 7), subplot_kw={"polar": True})
    for name, scores in data.items():
        values = [scores[k] for k in labels]
        values += 5055.baike.tongsou.com 
        ax.plot(angles, values, linewidth=2, label=name)
        ax.fill(angles, values, alpha=0.10)

    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(["信号质量", "安全性", "商业化准备度", "带宽", "监管友好度"], fontsize=11)
    ax.set_ylim(0, 1)
    ax.set_title("2026 脑机接口技术路线成熟度对比", pad=20)
    ax.legend(loc="upper right", bbox_to_anchor=(1.30, 1.05))
    plt.tight_layout( 5052.baike.tongsou.com )
    plt.savefig("bci_radar.png", dpi=150, bbox_inches="tight")
    plt.show()

if __name__ == "__main__":
    radar_plot(ROUTES)

"""
bci_market_model.py
测算脑机接口产业链价值分布与2026-2030市场规模
"""
import numpy as np
import matplotlib.pyplot as plt

# 产业链价值占比（参考2026年产业报告）
CHAIN_VALUE = {
    "上游（芯片/电极/算法）": 0.26,
    "中游（设备集成/软件）": 0.45,
    "下游（医疗/消费应用）": 0.29,
}

# 中国市场规模预测（亿元）
MARKET_FORECAST = {
    2023: 17.8,
    2024: 32.6,
    2025: 51.0,
    2026: 98.0,   # 商业化元年爆发
    2027: 145.0,
    2028: 210.0,
    2030: 350.0,
}

def plot_chain():
    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # 左图：产业链价值饼图
    ax1 = axes[ 5057.baike.tongsou.com ]
    colors = ["#2196F3", "#FF9800", "#4CAF50"]
    ax1.pie(CHAIN_VALUE.values(), labels=CHAIN_VALUE.keys(),
            autopct="%1.1f%%", colors=colors, startangle=90)
    ax1.set_title("脑机接口产业链价值分布")

    # 右图：市场规模增长曲线
    ax2 = axes[1]
    years = list(MARKET_FORECAST.keys())
    values = list(MARKET_FORECAST.values())
    ax2.plot(years, values, "o-", linewidth=2, markersize=8, color="#E91E63")
    ax2.fill_between(years, values, alpha=0.15, color="#E91E63")
    ax2.set_xlabel("年份")
    ax2.set_ylabel("市场规模（亿元）")
    ax2.set_title("中国脑机接口市场规模预测")
    ax2.grid(True, alpha=0.3)
    for y, v in zip(years, values): 6004.baike.tongsou.com 
        ax2.annotate(f"{v}亿", (y, v), textcoords="offset points",
                     xytext=(0, 10), ha="center", fontsize=9)

    plt.tight_layout( 6007.baike.tongsou.com )
    plt.savefig("bci_market.png", dpi=150, bbox_inches="tight")
    plt.show( 6008.baike.tongsou.com )

def cagr(start, end, years):
    return (end / start) ** (1 / years) - 1

if __name__ == "__main__":
    plot_chain()
    c = cagr(MARKET_FORECAST[2023], MARKET_FORECAST[2026], 3)
    print(f"2023→2026 CAGR: {c:.1%}")
    c2 = cagr(MARKET_FORECAST[2026], MARKET_FORECAST[2030], 4)
    print(f"2026→2030 CAGR: {c2:.1%}")

"""
bci_clinical_tracker.py
追踪2026年国内脑机接口关键临床/注册里程碑
"""
import pandas as pd
from datetime import datetime

MILESTONES = [
    {"date": "2026-03", "company": "博睿康", "event": "NEO-ONE SCI获NMPA三类证，全球首款侵入式BCI医疗器械上市", "route": "半侵入式"},
    {"date": "2026-05", "company": "智冉医疗", "event": "128通道全植入式系统启动多中心临床试验（LEAP研究）", "route": "全侵入式"},
    {"date": "2026-07", "company": "博睿康/华山医院", "event": "完成NEO系统全球首例商业化植入手术", "route": "半侵入式"},
    {"date": "2026-07", "company": "脑虎科技", "event": "“三全”系统进入NMPA创新医疗器械绿色通道", "route": "半侵入式"},
    {"date": "2026-09", "company": "复旦大学", "event": "脑脊接口完成全球首批4例临床验证，瘫痪患者两周恢复行走", "route": "脑脊接口"},
    {"date": "2026-09", "company": "博睿康", "event": "科创板IPO申请获受理，拟募资25亿元", "route": "半侵入式"},
]

def tracker():
    df = pd.DataFrame(MILESTONES)
    df["date"] = pd.to_datetime(df["date"])
    df = 14013.baike.tongsou.com 

    print("=" * 80)
    print("2026年中国脑机接口关键里程碑")
    print("=" * 80)
    for _, row in df.iterrows():
        print(f"[{row['date'].strftime('%Y-%m')}] {row['company']:8s} | {row['route']:8s}")
        print(f" 14068.baike.tongsou.com ")
        print( 14009.baike.tongsou.com )

    # 路线分布统计
    route_counts = df["route"].value_counts()
    print("技术路线分布：")
    for route, count in route_counts.items():
        bar = 5038.baike.tongsou.com 
        print(f"  {route:8s} {bar} ({count})")

if __name__ == "__main__":
    tracker()