---
id: wiki-2026-0508-neuroprosthetics-development
title: Neuroprosthetics Development
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Neuroprosthetics, BCI, Brain-Computer Interface, Neural Prosthesis]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [bci, neuroprosthetics, neuralink, synchron, cochlear, retinal-prosthesis, motor-bci]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack: { language: python, framework: pytorch-mne }
---

# Neuroprosthetics Development

## 매 한 줄
- 신경보철은 신경계와 직접 인터페이스하여 손실된 감각·운동·인지 기능을 복원하는 기기군이다(BCI, cochlear, retinal, motor prosthesis).

## 매 핵심
- **분류**: (1) 감각 보철(cochlear, retinal), (2) 운동 BCI(invasive Utah array, ECoG, Stentrode), (3) 인지/심부자극(DBS for PD, OCD).
- **2026 현황**: Neuralink N1 첫 인간 임플란트(2024) → 1024ch threadlike electrode + bluetooth, Synchron Stentrode(stent 형태, 정맥경유, 16ch), Blackrock Utah array(96–256ch, gold standard), Cochlear Nucleus 8(~22 ch electrode).
- **신호 처리 파이프라인**: spike sorting → feature(firing rate, LFP power) → Kalman/RNN decoder → effector(cursor, robot arm, speech).
- **Motor BCI breakthrough**: ALS 환자 speech BCI(Stanford 2023) ~62 wpm, BrainGate 8자유도 로봇팔.
- **재료/수명**: 만성 임플란트 6–12개월 후 glial encapsulation, signal degradation. 유연 polymer probe(NeuroNexus, Paradromics)로 개선.

## 💻 패턴
```python
# Spike sorting with template matching (simplified)
import numpy as np
from scipy.signal import butter, filtfilt

def bandpass(x, fs=30000, lo=300, hi=6000):
    b, a = butter(4, [lo / (fs / 2), hi / (fs / 2)], btype="band")
    return filtfilt(b, a, x)

def detect_spikes(x, thresh_sd=4):
    thr = -thresh_sd * np.median(np.abs(x)) / 0.6745
    return np.where(x < thr)[0]
```

```python
# Kalman filter decoder: neural firing → cursor velocity
import numpy as np

class KalmanDecoder:
    def __init__(self, A, C, W, Q):
        self.A, self.C, self.W, self.Q = A, C, W, Q
        self.x = np.zeros(A.shape[0])
        self.P = np.eye(A.shape[0])

    def step(self, y):
        # predict
        self.x = self.A @ self.x
        self.P = self.A @ self.P @ self.A.T + self.W
        # update
        K = self.P @ self.C.T @ np.linalg.inv(self.C @ self.P @ self.C.T + self.Q)
        self.x = self.x + K @ (y - self.C @ self.x)
        self.P = (np.eye(len(self.x)) - K @ self.C) @ self.P
        return self.x  # [vx, vy]
```

```python
# RNN decoder for speech BCI (handwriting/speech-to-text from cortex)
import torch, torch.nn as nn

class CortexRNN(nn.Module):
    def __init__(self, n_channels=256, hidden=512, n_phonemes=39):
        super().__init__()
        self.rnn = nn.GRU(n_channels, hidden, num_layers=2, batch_first=True)
        self.head = nn.Linear(hidden, n_phonemes)

    def forward(self, x):  # (B, T, C)
        h, _ = self.rnn(x)
        return self.head(h)  # CTC loss downstream
```

```python
# Cochlear implant: CIS strategy (continuous interleaved sampling)
import numpy as np
from scipy.signal import hilbert

def cis_encode(audio, n_channels=22, fs=16000):
    bands = np.linspace(200, 8000, n_channels + 1)
    pulses = []
    for i in range(n_channels):
        # bandpass + envelope (Hilbert)
        from scipy.signal import butter, filtfilt
        b, a = butter(4, [bands[i] / (fs / 2), bands[i + 1] / (fs / 2)], btype="band")
        env = np.abs(hilbert(filtfilt(b, a, audio)))
        pulses.append(env)
    return np.array(pulses)  # delivered as biphasic pulse trains
```

```python
# Retinal prosthesis (Argus II-style): downsample + polarity coding
import numpy as np

def retinal_encode(image_gray, n_electrodes=60):
    h, w = image_gray.shape
    grid = int(np.sqrt(n_electrodes))
    block_h, block_w = h // grid, w // grid
    out = np.zeros((grid, grid))
    for i in range(grid):
        for j in range(grid):
            out[i, j] = image_gray[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w].mean()
    return out  # → electrode current amplitude
```

```python
# Closed-loop DBS: detect beta burst (PD) and trigger stimulation
def beta_burst_trigger(lfp, fs=1000, lo=13, hi=30, thresh=2.0):
    from scipy.signal import butter, filtfilt
    import numpy as np
    b, a = butter(4, [lo / (fs / 2), hi / (fs / 2)], btype="band")
    beta = filtfilt(b, a, lfp)
    env = np.abs(beta)
    return env > thresh * env.std()  # boolean per sample
```

```python
# Online recalibration: ridge regression refit every block
from sklearn.linear_model import Ridge
def recalibrate(X_block, y_block, alpha=1.0):
    return Ridge(alpha=alpha).fit(X_block, y_block)
```

## 매 결정 기준
- **침습 vs 비침습**: 고대역폭(speech, robot arm) → invasive(Utah, Neuralink). 보조 통신·간단 cursor → ECoG/Stentrode.
- **Decoder**: 저차원 cursor → Kalman. 고차원 sequence(speech, handwriting) → RNN/Transformer + CTC.
- **재료**: 단기 임상 → silicon Utah. 만성·유연성 → polyimide/PEDOT:PSS.
- **윤리/규제**: FDA IDE, IRB, informed consent. 결과 발표 전 explanted device 분석 필수.

## 🔗 Graph
- 관련: [[Neurorehabilitation-Post-Stroke]], [[Neuroplasticity]]

## 🤖 LLM 활용
- 임상 protocol 초안 검토(IRB 양식 비교).
- 임플란트 후 환자 보고 데이터 요약.
- decoder hyperparameter 탐색 제안(grid spec).

## ❌ 안티패턴
- 비정형 spike sorting을 임상 결정에 직접 사용.
- 만성 임플란트 noise drift 보정 없는 고정 decoder.
- 환자 home use에서 fail-safe(자극 정지 버튼) 부재.

## 🧪 검증
- BCI bench: cursor BPS(bits-per-second), word error rate(speech BCI).
- 안전: impedance trend, infection rate, MRI compatibility.

## 🕓 Changelog
- 2026-05-08 Phase 1: 초안 자동 생성.
- 2026-05-10 Manual cleanup: 본문 보강, Neuralink/Synchron 2026 현황 반영, 코드 패턴 7개.
