python中眼动追踪数据分析（Eye-link）

Question

我有来自眼动追踪的数据（.edf 文件 - 来自 SR-research 的 Eyelink）。我想对其进行分析并获得各种措施，例如注视、扫视、持续时间等。 是否有现有的包来分析眼动追踪数据？ 谢谢！

Answer 1

嘿，这个问题似乎很老了，但也许我可以重新激活它，因为我目前面临同样的情况。 首先，我建议您将 .edf 转换为 .asc 文件。这样更容易阅读以获得第一印象。 为此，存在许多工具，但我使用了 SR-Research Eyelink 开发人员工具包 (here)。

我不知道您的设置，但 Eyelink 1000 本身可以检测扫视和注视。我在 .asc 文件中的情况如下所示：

SFIX L   10350642
10350642      864.3   542.7  2317.0
...
...
10350962      863.2   540.4  2354.0
EFIX L   10350642   10350962    322   863.1   541.2    2339
SSACC L  10350964
10350964      863.4   539.8  2359.0
...
...
10351004      683.4   511.2  2363.0
ESACC L  10350964   10351004    42    863.4   539.8   683.4   511.2    5.79     221

第一个数字对应时间戳，第二个和第三个对应x-y坐标，最后一个是你的瞳孔直径（ESACC后面的最后一个数字是什么，我不知道）。

SFIX -> start fixation
EFIX -> end fixation
SSACC -> start saccade
ESACC -> end saccade

你也可以看看 PyGaze，我没用过，但是搜索工具箱，总是弹出这个。

编辑 我找到了这个工具箱here。它看起来很酷，并且适用于示例数据，但遗憾的是不适用于我的

第 2 条编辑 在处理我自己的眼动追踪数据后重新审视这个问题，我想我可能会分享一个编写的函数来处理我的数据：

def eyedata2pandasframe(directory):
'''
This function takes a directory from which it tries to read in ASCII files containing eyetracking data
It returns  eye_data: A pandas dataframe containing data from fixations AND saccades fix_data: A pandas dataframe containing only data from fixations
            sac_data: pandas dataframe containing only data from saccades
            fixation: numpy array containing information about fixation onsets and offsets
            saccades: numpy array containing information about saccade onsets and offsets
            blinks: numpy array containing information about blink onsets and offsets 
            trials: numpy array containing information about trial onsets 
'''
eye_data= []
fix_data = []
sac_data = []
data_header = {0: 'TimeStamp',1: 'X_Coord',2: 'Y_Coord',3: 'Diameter'}
event_header = {0: 'Start', 1: 'End'}
start_reading = False
in_blink = False
in_saccade = False
fix_timestamps = []
sac_timestamps = []
blink_timestamps = []
trials = []
sample_rate_info = []
sample_rate = 0
# read the file and store, depending on the messages the data
# we have the following structure:
# a header -- every line starts with a '**'
# a bunch of messages containing information about callibration/validation and so on all starting with 'MSG'
# followed by:
# START 10350638    LEFT    SAMPLES EVENTS
# PRESCALER 1
# VPRESCALER    1
# PUPIL AREA
# EVENTS    GAZE    LEFT    RATE     500.00 TRACKING    CR  FILTER  2
# SAMPLES   GAZE    LEFT    RATE     500.00 TRACKING    CR  FILTER  2
# followed by the actual data:
# normal data --> [TIMESTAMP]\t [X-Coords]\t [Y-Coords]\t [Diameter]
# Start of EVENTS [BLINKS FIXATION SACCADES] --> S[EVENTNAME] [EYE] [TIMESTAMP]
# End of EVENTS --> E[EVENT] [EYE] [TIMESTAMP_START]\t [TIMESTAMP_END]\t [TIME OF EVENT]\t [X-Coords start]\t [Y-Coords start]\t [X_Coords end]\t [Y-Coords end]\t [?]\t [?]
# Trial messages --> MSG timestamp\t TRIAL [TRIALNUMBER]
try:
    with open(directory) as f:
        csv_reader = csv.reader(f, delimiter ='\t')
        for i, row in enumerate (csv_reader):
            if any ('RATE' in item for item in row):
                sample_rate_info = row
            if any('SYNCTIME' in item for item in row):          # only start reading after this message
                start_reading = True
            elif any('SFIX' in item for item in row): pass
                #fix_timestamps[0].append (row)
            elif any('EFIX' in item for item in row):
                fix_timestamps.append ([row[0].split(' ')[4],row[1]])
                #fix_timestamps[1].append (row)
            elif any('SSACC' in item for item in row): 
                #sac_timestamps[0].append (row)
                in_saccade = True
            elif any('ESACC' in item for item in row):
                sac_timestamps.append ([row[0].split(' ')[3],row[1]])
                in_saccade = False
            elif any('SBLINK' in item for item in row):          # stop reading here because the blinks contain NaN
                # blink_timestamps[0].append (row)
                in_blink = True
            elif any('EBLINK' in item for item in row):          # start reading again. the blink ended
                blink_timestamps.append ([row[0].split(' ')[2],row[1]])
                in_blink = False
            elif any('TRIAL' in item for item in row):
                # the first element is 'MSG', we don't need it, then we split the second element to seperate the timestamp and only keep it as an integer
                trials.append (int(row[1].split(' ')[0]))
            elif start_reading and not in_blink:
                eye_data.append(row)
                if in_saccade:
                    sac_data.append(row)
                else:
                    fix_data.append(row)

    # drop the last data point, because it is the 'END' message
    eye_data.pop(-1)
    sac_data.pop(-1)
    fix_data.pop(-1)
    # convert every item in list into a float, substract the start of the first trial to set the start of the first video to t0=0
    # then devide by 1000 to convert from milliseconds to seconds
    for row in eye_data:
        for i, item in enumerate (row):
            row[i] = float (item)

    for row in fix_data:
        for i, item in enumerate (row):
            row[i] = float (item)

    for row in sac_data:
        for i, item in enumerate (row):
            row[i] = float (item)

    for row in fix_timestamps:
        for i, item in enumerate (row):
            row [i] = (float(item)-trials[0])/1000

    for row in sac_timestamps:
        for i, item in enumerate (row):
            row [i] = (float(item)-trials[0])/1000

    for row in blink_timestamps:
        for i, item in enumerate (row):
            row [i] = (float(item)-trials[0])/1000

    sample_rate = float (sample_rate_info[4])

    # convert into pandas fix_data Frames for a better overview
    eye_data = pd.DataFrame(eye_data)
    fix_data = pd.DataFrame(fix_data)
    sac_data = pd.DataFrame(sac_data)
    fix_timestamps = pd.DataFrame(fix_timestamps)
    sac_timestamps = pd.DataFrame(sac_timestamps)
    trials = np.array(trials)
    blink_timestamps = pd.DataFrame(blink_timestamps)
    # rename header for an even better overview
    eye_data = eye_data.rename(columns=data_header)
    fix_data = fix_data.rename(columns=data_header)
    sac_data = sac_data.rename(columns=data_header)
    fix_timestamps = fix_timestamps.rename(columns=event_header)
    sac_timestamps = sac_timestamps.rename(columns=event_header)
    blink_timestamps = blink_timestamps.rename(columns=event_header)
    # substract the first timestamp of trials to set the start of the first video to t0=0
    eye_data.TimeStamp -= trials[0]
    fix_data.TimeStamp -= trials[0]
    sac_data.TimeStamp -= trials[0]
    trials -= trials[0]
    trials = trials /1000      # does not work with trials/=1000
    # devide TimeStamp to get time in seconds
    eye_data.TimeStamp /=1000
    fix_data.TimeStamp /=1000
    sac_data.TimeStamp /=1000
    return eye_data, fix_data, sac_data, fix_timestamps, sac_timestamps, blink_timestamps, trials, sample_rate
except:
    print ('Could not read ' + str(directory) + ' properly!!! Returned empty data')
    return eye_data, fix_data, sac_data, fix_timestamps, sac_timestamps, blink_timestamps, trials, sample_rate

希望对大家有所帮助。您可能需要更改代码的某些部分，例如拆分字符串以获取有关事件开启/偏移的重要信息的索引。或者您不想将时间戳转换为秒，或者不想将第一次试用的开始时间设置为 0。这取决于您。 此外，在我的数据中，我们发送了一条消息以了解我们何时开始测量（'SYNCTIME'），而我的实验中只有一个条件，所以只有一个'TRIAL'消息

干杯

Answer 2

pyeparse 似乎是另一个（但目前似乎未维护）可用于 eyelink 数据分析的库。

以下是他们示例的简短摘录：

import numpy as np
import matplotlib.pyplot as plt

import pyeparse as pp

fname = '../pyeparse/tests/data/test_raw.edf'

raw = pp.read_raw(fname)

# visualize initial calibration
raw.plot_calibration(title='5-Point Calibration')

# create heatmap
raw.plot_heatmap(start=3., stop=60.)

编辑：在我发布答案后，我发现了一个很好的列表，其中包含许多用于 eyelink edf 数据分析的潜在工具：https://github.com/davebraze/FDBeye/wiki/Researcher-Contributed-Eye-Tracking-Tools

Answer 3

至少要将 .edf 文件导入 pandas DF，您可以使用 Niklas Wilming 提供的以下软件包：https://github.com/nwilming/pyedfread/tree/master/pyedfread
这应该已经处理了扫视和注视 - 看看自述文件。一旦它们进入数据框，您就可以对其应用任何您想要的分析。