Examples

Knight Board

Knight Board — full session

import time
from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds

# ── 1. Configure ──────────────────────────────────────────────────────────────
params = BrainFlowInputParams()
params.serial_port = "COM3"           # adjust for your OS
params.other_info  = '{"gain": 12}'   # must match gain used in chon_ commands

board = BoardShim(BoardIds.NEUROPAWN_KNIGHT_BOARD, params)
board_id = board.get_board_id()

eeg_channels  = BoardShim.get_eeg_channels(board_id)
sampling_rate = BoardShim.get_sampling_rate(board_id)   # 125

# ── 2. Open session & start stream ───────────────────────────────────────────
board.prepare_session()
board.start_stream(450000)
time.sleep(2)                          # wait for the board to stabilise

# ── 3. Enable channels ────────────────────────────────────────────────────────
# Channels are OFF by default. Both the Knight Board and Knight Board IMU
# require chon_ commands after start_stream() — without them you will see
# no signal data.
for ch in range(1, 9):
    time.sleep(0.5)
    board.config_board(f"chon_{ch}_12")
    time.sleep(1)
    board.config_board(f"rldadd_{ch}")
    time.sleep(0.5)

# ── 4. Read data ──────────────────────────────────────────────────────────────
try:
    while True:
        time.sleep(1)
        data     = board.get_current_board_data(sampling_rate * 1)
        eeg_data = data[eeg_channels, :]  # shape: (8, num_samples), µV
        print(f"Received {eeg_data.shape[1]} samples")

# ── 5. Clean up ───────────────────────────────────────────────────────────────
finally:
    board.stop_stream()
    board.release_session()

---

Knight Board IMU

The IMU variant uses NEUROPAWN_KNIGHT_BOARD_IMU. Channel activation is identical to the standard board — chon_ and rldadd_ commands are still required after start_stream(). IMU data is available on top of the standard EEG channels.

Note

See Knight Board IMU for the full list of IMU channel indices.

Knight Board IMU — full session

import time
from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds

ACCEL_CHANNELS = [11, 12, 13]   # x, y, z
GYRO_CHANNELS  = [14, 15, 16]   # x, y, z
MAG_CHANNELS   = [17, 18, 19]   # x, y, z

params = BrainFlowInputParams()
params.serial_port = "COM3"
params.other_info  = '{"gain": 12}'

board = BoardShim(BoardIds.NEUROPAWN_KNIGHT_BOARD_IMU, params)
board_id = board.get_board_id()

eeg_channels  = BoardShim.get_eeg_channels(board_id)
sampling_rate = BoardShim.get_sampling_rate(board_id)

board.prepare_session()
board.start_stream(450000)
time.sleep(2)

for ch in range(1, 9):
    time.sleep(0.5)
    board.config_board(f"chon_{ch}_12")
    time.sleep(1)
    board.config_board(f"rldadd_{ch}")
    time.sleep(0.5)

try:
    while True:
        time.sleep(1)
        data = board.get_current_board_data(sampling_rate * 1)

        eeg_data   = data[eeg_channels,    :]   # (8, n) µV
        accel_data = data[ACCEL_CHANNELS,  :]   # (3, n)
        gyro_data  = data[GYRO_CHANNELS,   :]   # (3, n)
        mag_data   = data[MAG_CHANNELS,    :]   # (3, n)

        print(f"EEG samples : {eeg_data.shape[1]}")
        print(f"Accel X mean: {accel_data[0].mean():.4f}")

finally:
    board.stop_stream()
    board.release_session()