实验室流层 (LSL) 用于同步多个数据流
罗希尼·兰德尼亚
2025年10月1日
分享：
由 Roshini Randeniya 和 Lucas Kleine 撰写
操作：
在命令行中运行后，此脚本立即启动 LSL 流。每当按下 'Enter' 键时，它会发送一个触发器并播放音频文件。"""
import sounddevice as sd
import soundfile as sf
from pylsl import StreamInfo, StreamOutlet
def wait_for_keypress():
print("按 ENTER 开始音频播放并发送 LSL 标记。")
while True: # This loop waits for a keyboard input input_str = input() # Wait for input from the terminal if input_str == "": # If the enter key is pressed, proceed break
def AudioMarker(audio_file, outlet): # 播放音频并发送标记的函数
data, fs = sf.read(audio_file) # 加载音频文件
print("Playing audio and sending LSL marker...") marker_val = [1] outlet.push_sample(marker_val) # Send marker indicating the start of audio playback sd.play(data, fs) # play the audio sd.wait() # Wait until audio is done playing print("Audio playback finished.")
if name == "main": # 主循环
# 设置 LSL 流用于标记
stream_name = 'AudioMarkers'
stream_type = 'Markers'
n_chans = 1
sr = 0 # 设置为 0 采样率，因为标记不规则
chan_format = 'int32'
marker_id = 'uniqueMarkerID12345'
info = StreamInfo(stream_name, stream_type, n_chans, sr, chan_format, marker_id) outlet = StreamOutlet(info) # create LSL outlet # Keep the script running and wait for ENTER key to play audio and send marker while True: wait_for_keypress() audio_filepath = "/path/to/your/audio_file.wav" # replace with correct path to your audio file AudioMarker(audio_filepath, outlet) # After playing audio and sending a marker, the script goes back to waiting for the next keypress</code></pre><p><em><strong>**By running this file (even before playing the audio), you've initiated an LSL stream through an outlet</strong></em><strong>. Now we'll view that stream in LabRecorder</strong></p><p><strong>STEP 5 - Use LabRecorder to view and save all LSL streams</strong></p><ol><li data-preset-tag="p"><p>Open LabRecorder</p></li><li data-preset-tag="p"><p>Press <em><strong>Update</strong></em>. The available LSL streams should be visible in the stream list<br> • You should be able to see streams from both EmotivPROs (usually called "EmotivDataStream") and the marker stream (called "AudioMarkers")</p></li><li data-preset-tag="p"><p>Click <em><strong>Browse</strong></em> to select a location to store data (and set other parameters)</p></li><li data-preset-tag="p"><p>Select all streams and press <em><strong>Record</strong></em> to start recording</p></li><li data-preset-tag="p"><p>Click Stop when you want to end the recording</p></li></ol><p><br></p><img alt="" src="https://framerusercontent.com/images/HFGuJF9ErVu2Jxrgtqt11tl0No.jpg"><h2><strong>Working with the data</strong></h2><p><strong>LabRecorder outputs an XDF file (Extensible Data Format) that contains data from all the streams. XDF files are structured into, </strong><em><strong>streams</strong></em><strong>, each with a different </strong><em><strong>header</strong></em><strong> that describes what it contains (device name, data type, sampling rate, channels, and more). You can use the below codeblock to open your XDF file and display some basic information.</strong></p><pre data-language="JSX"><code>
**运行此文件（即使在播放音频之前），您已通过 outlet 启动了 LSL 流。现在我们将在 LabRecorder 中查看该流。
步骤 5 - 使用 LabRecorder 查看和保存所有 LSL 流
打开 LabRecorder
按 更新。可用的 LSL 流应该在流列表中可见
• 您应该能看到来自两个 EmotivPRO 的流（通常称为 "EmotivDataStream"）以及标记流（称为 "AudioMarkers"）点击 浏览 选择保存数据的位置（并设置其他参数）
选择所有流并按 记录 开始录制
点击停止以结束录制
处理数据
LabRecorder 输出一个 XDF 文件（可扩展数据格式），包含来自所有流的数据。XDF 文件被结构化为，流，每个流都有不同的头部，描述其包含的内容（设备名称、数据类型、采样率、通道等）。您可以使用下面的代码块打开您的 XDF 文件并显示一些基本信息。
此示例脚本演示了一些基本功能，用于导入和注释从 EmotivPRO 软件收集的 EEG 数据。它使用 MNE 加载 XDF 文件，打印一些基本元数据，创建一个 info 对象并绘制功率谱图。"""
import pyxdf
import mne
import matplotlib.pyplot as plt
import numpy as np
您的 XDF 文件路径
data_path = '/path/to/your/xdf_file.xdf'
加载 XDF 文件
streams, fileheader = pyxdf.load_xdf(data_path)
print("XDF 文件头：", fileheader)
print("找到的流数量：", len(streams))
for i, stream in enumerate(streams):
print("\n流", i + 1)
print("流名称:", stream['info']['name'][0])
print("流类型:", stream['info']['type'][0])
print("通道数量:", stream['info']['channel_count'][0])
sfreq = float(stream['info']['nominal_srate'][0])
print("采样率:", sfreq)
print("样本数量:", len(stream['time_series']))
print("打印前 5 个数据点:", stream['time_series'][:5])
channel_names = [chan['label'][0] for chan in stream['info']['desc'][0]['channels'][0]['channel']] print("Channel Names:", channel_names) channel_types = 'eeg'
创建 MNE info 对象
info = mne.create_info(channel_names, sfreq, channel_types)
data = np.array(stream['time_series']).T # 数据需要转置：通道 x 样本
raw = mne.io.RawArray(data, info)
raw.plot_psd(fmax=50) # 绘制简单的谱图（功率谱密度）
附加资源从 EMOTIV GitHub 下载该教程作为 Jupyter 笔记本查阅 LSL 在线文档，包括 GitHub 上的官方 README 文件您将需要一个或多个受支持的数据采集设备来收集数据所有 EMOTIV 的脑电设备都连接到具有 LSL 内置功能的 EmotivPRO 软件，用于发送和接收数据流附加资源：使用 Emotiv 设备运行 LSL 的代码，示例脚本YouTube 上的 LSL 演示SCCN LSL GitHub 存储库中的所有关联库用于子模块和应用程序的 GitHub 存储库HyPyP 分析管道用于超扫描研究
由 Roshini Randeniya 和 Lucas Kleine 撰写
操作：
在命令行中运行后，此脚本立即启动 LSL 流。每当按下 'Enter' 键时，它会发送一个触发器并播放音频文件。"""
import sounddevice as sd
import soundfile as sf
from pylsl import StreamInfo, StreamOutlet
def wait_for_keypress():
print("按 ENTER 开始音频播放并发送 LSL 标记。")
while True: # This loop waits for a keyboard input input_str = input() # Wait for input from the terminal if input_str == "": # If the enter key is pressed, proceed break
def AudioMarker(audio_file, outlet): # 播放音频并发送标记的函数
data, fs = sf.read(audio_file) # 加载音频文件
print("Playing audio and sending LSL marker...") marker_val = [1] outlet.push_sample(marker_val) # Send marker indicating the start of audio playback sd.play(data, fs) # play the audio sd.wait() # Wait until audio is done playing print("Audio playback finished.")
if name == "main": # 主循环
# 设置 LSL 流用于标记
stream_name = 'AudioMarkers'
stream_type = 'Markers'
n_chans = 1
sr = 0 # 设置为 0 采样率，因为标记不规则
chan_format = 'int32'
marker_id = 'uniqueMarkerID12345'
info = StreamInfo(stream_name, stream_type, n_chans, sr, chan_format, marker_id) outlet = StreamOutlet(info) # create LSL outlet # Keep the script running and wait for ENTER key to play audio and send marker while True: wait_for_keypress() audio_filepath = "/path/to/your/audio_file.wav" # replace with correct path to your audio file AudioMarker(audio_filepath, outlet) # After playing audio and sending a marker, the script goes back to waiting for the next keypress</code></pre><p><em><strong>**By running this file (even before playing the audio), you've initiated an LSL stream through an outlet</strong></em><strong>. Now we'll view that stream in LabRecorder</strong></p><p><strong>STEP 5 - Use LabRecorder to view and save all LSL streams</strong></p><ol><li data-preset-tag="p"><p>Open LabRecorder</p></li><li data-preset-tag="p"><p>Press <em><strong>Update</strong></em>. The available LSL streams should be visible in the stream list<br> • You should be able to see streams from both EmotivPROs (usually called "EmotivDataStream") and the marker stream (called "AudioMarkers")</p></li><li data-preset-tag="p"><p>Click <em><strong>Browse</strong></em> to select a location to store data (and set other parameters)</p></li><li data-preset-tag="p"><p>Select all streams and press <em><strong>Record</strong></em> to start recording</p></li><li data-preset-tag="p"><p>Click Stop when you want to end the recording</p></li></ol><p><br></p><img alt="" src="https://framerusercontent.com/images/HFGuJF9ErVu2Jxrgtqt11tl0No.jpg"><h2><strong>Working with the data</strong></h2><p><strong>LabRecorder outputs an XDF file (Extensible Data Format) that contains data from all the streams. XDF files are structured into, </strong><em><strong>streams</strong></em><strong>, each with a different </strong><em><strong>header</strong></em><strong> that describes what it contains (device name, data type, sampling rate, channels, and more). You can use the below codeblock to open your XDF file and display some basic information.</strong></p><pre data-language="JSX"><code>
**运行此文件（即使在播放音频之前），您已通过 outlet 启动了 LSL 流。现在我们将在 LabRecorder 中查看该流。
步骤 5 - 使用 LabRecorder 查看和保存所有 LSL 流
打开 LabRecorder
按 更新。可用的 LSL 流应该在流列表中可见
• 您应该能看到来自两个 EmotivPRO 的流（通常称为 "EmotivDataStream"）以及标记流（称为 "AudioMarkers"）点击 浏览 选择保存数据的位置（并设置其他参数）
选择所有流并按 记录 开始录制
点击停止以结束录制
处理数据
LabRecorder 输出一个 XDF 文件（可扩展数据格式），包含来自所有流的数据。XDF 文件被结构化为，流，每个流都有不同的头部，描述其包含的内容（设备名称、数据类型、采样率、通道等）。您可以使用下面的代码块打开您的 XDF 文件并显示一些基本信息。
此示例脚本演示了一些基本功能，用于导入和注释从 EmotivPRO 软件收集的 EEG 数据。它使用 MNE 加载 XDF 文件，打印一些基本元数据，创建一个 info 对象并绘制功率谱图。"""
import pyxdf
import mne
import matplotlib.pyplot as plt
import numpy as np
您的 XDF 文件路径
data_path = '/path/to/your/xdf_file.xdf'
加载 XDF 文件
streams, fileheader = pyxdf.load_xdf(data_path)
print("XDF 文件头：", fileheader)
print("找到的流数量：", len(streams))
for i, stream in enumerate(streams):
print("\n流", i + 1)
print("流名称:", stream['info']['name'][0])
print("流类型:", stream['info']['type'][0])
print("通道数量:", stream['info']['channel_count'][0])
sfreq = float(stream['info']['nominal_srate'][0])
print("采样率:", sfreq)
print("样本数量:", len(stream['time_series']))
print("打印前 5 个数据点:", stream['time_series'][:5])
channel_names = [chan['label'][0] for chan in stream['info']['desc'][0]['channels'][0]['channel']] print("Channel Names:", channel_names) channel_types = 'eeg'
创建 MNE info 对象
info = mne.create_info(channel_names, sfreq, channel_types)
data = np.array(stream['time_series']).T # 数据需要转置：通道 x 样本
raw = mne.io.RawArray(data, info)
raw.plot_psd(fmax=50) # 绘制简单的谱图（功率谱密度）
附加资源从 EMOTIV GitHub 下载该教程作为 Jupyter 笔记本查阅 LSL 在线文档，包括 GitHub 上的官方 README 文件您将需要一个或多个受支持的数据采集设备来收集数据所有 EMOTIV 的脑电设备都连接到具有 LSL 内置功能的 EmotivPRO 软件，用于发送和接收数据流附加资源：使用 Emotiv 设备运行 LSL 的代码，示例脚本YouTube 上的 LSL 演示SCCN LSL GitHub 存储库中的所有关联库用于子模块和应用程序的 GitHub 存储库HyPyP 分析管道用于超扫描研究
由 Roshini Randeniya 和 Lucas Kleine 撰写
操作：
在命令行中运行后，此脚本立即启动 LSL 流。每当按下 'Enter' 键时，它会发送一个触发器并播放音频文件。"""
import sounddevice as sd
import soundfile as sf
from pylsl import StreamInfo, StreamOutlet
def wait_for_keypress():
print("按 ENTER 开始音频播放并发送 LSL 标记。")
while True: # This loop waits for a keyboard input input_str = input() # Wait for input from the terminal if input_str == "": # If the enter key is pressed, proceed break
def AudioMarker(audio_file, outlet): # 播放音频并发送标记的函数
data, fs = sf.read(audio_file) # 加载音频文件
print("Playing audio and sending LSL marker...") marker_val = [1] outlet.push_sample(marker_val) # Send marker indicating the start of audio playback sd.play(data, fs) # play the audio sd.wait() # Wait until audio is done playing print("Audio playback finished.")
if name == "main": # 主循环
# 设置 LSL 流用于标记
stream_name = 'AudioMarkers'
stream_type = 'Markers'
n_chans = 1
sr = 0 # 设置为 0 采样率，因为标记不规则
chan_format = 'int32'
marker_id = 'uniqueMarkerID12345'
info = StreamInfo(stream_name, stream_type, n_chans, sr, chan_format, marker_id) outlet = StreamOutlet(info) # create LSL outlet # Keep the script running and wait for ENTER key to play audio and send marker while True: wait_for_keypress() audio_filepath = "/path/to/your/audio_file.wav" # replace with correct path to your audio file AudioMarker(audio_filepath, outlet) # After playing audio and sending a marker, the script goes back to waiting for the next keypress</code></pre><p><em><strong>**By running this file (even before playing the audio), you've initiated an LSL stream through an outlet</strong></em><strong>. Now we'll view that stream in LabRecorder</strong></p><p><strong>STEP 5 - Use LabRecorder to view and save all LSL streams</strong></p><ol><li data-preset-tag="p"><p>Open LabRecorder</p></li><li data-preset-tag="p"><p>Press <em><strong>Update</strong></em>. The available LSL streams should be visible in the stream list<br> • You should be able to see streams from both EmotivPROs (usually called "EmotivDataStream") and the marker stream (called "AudioMarkers")</p></li><li data-preset-tag="p"><p>Click <em><strong>Browse</strong></em> to select a location to store data (and set other parameters)</p></li><li data-preset-tag="p"><p>Select all streams and press <em><strong>Record</strong></em> to start recording</p></li><li data-preset-tag="p"><p>Click Stop when you want to end the recording</p></li></ol><p><br></p><img alt="" src="https://framerusercontent.com/images/HFGuJF9ErVu2Jxrgtqt11tl0No.jpg"><h2><strong>Working with the data</strong></h2><p><strong>LabRecorder outputs an XDF file (Extensible Data Format) that contains data from all the streams. XDF files are structured into, </strong><em><strong>streams</strong></em><strong>, each with a different </strong><em><strong>header</strong></em><strong> that describes what it contains (device name, data type, sampling rate, channels, and more). You can use the below codeblock to open your XDF file and display some basic information.</strong></p><pre data-language="JSX"><code>
**运行此文件（即使在播放音频之前），您已通过 outlet 启动了 LSL 流。现在我们将在 LabRecorder 中查看该流。
步骤 5 - 使用 LabRecorder 查看和保存所有 LSL 流
打开 LabRecorder
按 更新。可用的 LSL 流应该在流列表中可见
• 您应该能看到来自两个 EmotivPRO 的流（通常称为 "EmotivDataStream"）以及标记流（称为 "AudioMarkers"）点击 浏览 选择保存数据的位置（并设置其他参数）
选择所有流并按 记录 开始录制
点击停止以结束录制
处理数据
LabRecorder 输出一个 XDF 文件（可扩展数据格式），包含来自所有流的数据。XDF 文件被结构化为，流，每个流都有不同的头部，描述其包含的内容（设备名称、数据类型、采样率、通道等）。您可以使用下面的代码块打开您的 XDF 文件并显示一些基本信息。
此示例脚本演示了一些基本功能，用于导入和注释从 EmotivPRO 软件收集的 EEG 数据。它使用 MNE 加载 XDF 文件，打印一些基本元数据，创建一个 info 对象并绘制功率谱图。"""
import pyxdf
import mne
import matplotlib.pyplot as plt
import numpy as np
您的 XDF 文件路径
data_path = '/path/to/your/xdf_file.xdf'
加载 XDF 文件
streams, fileheader = pyxdf.load_xdf(data_path)
print("XDF 文件头：", fileheader)
print("找到的流数量：", len(streams))
for i, stream in enumerate(streams):
print("\n流", i + 1)
print("流名称:", stream['info']['name'][0])
print("流类型:", stream['info']['type'][0])
print("通道数量:", stream['info']['channel_count'][0])
sfreq = float(stream['info']['nominal_srate'][0])
print("采样率:", sfreq)
print("样本数量:", len(stream['time_series']))
print("打印前 5 个数据点:", stream['time_series'][:5])
channel_names = [chan['label'][0] for chan in stream['info']['desc'][0]['channels'][0]['channel']] print("Channel Names:", channel_names) channel_types = 'eeg'
创建 MNE info 对象
info = mne.create_info(channel_names, sfreq, channel_types)
data = np.array(stream['time_series']).T # 数据需要转置：通道 x 样本
raw = mne.io.RawArray(data, info)
raw.plot_psd(fmax=50) # 绘制简单的谱图（功率谱密度）
附加资源从 EMOTIV GitHub 下载该教程作为 Jupyter 笔记本查阅 LSL 在线文档，包括 GitHub 上的官方 README 文件您将需要一个或多个受支持的数据采集设备来收集数据所有 EMOTIV 的脑电设备都连接到具有 LSL 内置功能的 EmotivPRO 软件，用于发送和接收数据流附加资源：使用 Emotiv 设备运行 LSL 的代码，示例脚本YouTube 上的 LSL 演示SCCN LSL GitHub 存储库中的所有关联库用于子模块和应用程序的 GitHub 存储库HyPyP 分析管道用于超扫描研究
