Reflexive startle electromyography



What does this channel measure?

People exhibit in response to a sudden threatening event a whole body startle response, including increased heart rate, contraction of the neck muscles, and an eyeblink, among others. Measuring the startle response is important in the context of emotion research, as its magnitude is modulated by affective valence. Roughly, the more negative someone feels the larger the magnitude of the startle response. Typically startle responses are provoked using loud (95 dB), sudden (50 ms) bursts of white noise – so-called startle probes.

As an indicator of startle magnitude we use the strength of the eyeblink response, which is measured with electromyographic (EMG) activation of the lower eyelid. ANSLAB preprocesses and displays this EMG activation and allows to edit it for individual startle probes.
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Analyzing startle data

Starting with ANSLAB 2.4, there are two different ways of analyzing startle data:
  1. The raw data file contains a marker channel that indicates when the startle probe was given ("channel").
  2. The timepoints of the startle probes are given in a timing file ("timingfile").
You have to specify either "channel" or "timingfile" in the startle options, using the marker type dropdown box. For the case of a marker channel, the marker needs to go up when the noise burst starts and down when the noise burst ends. After loading the data file containing the EMG- and the marker-signal, you are prompted for this threshold (see image to the right).

Every timepoint, for which the marker channel rises above this threshold, is considered to be the onset of a startle tone. For the case of timing files, startle onsets are taken from an associated timingfile. All entries in the timing files are considered as startle trials independent of the segment value.

Next, the first startle probe (=trial) is displayed:
Here is how to read the startle data display window: Note that when setting the analysis type to 'startle', the 'dynamic'-section changed to show the startle scoring controls (see right above image).

Adjusting the detection parameters

In the example below, the baseline interval and the response window are not adequate for the data: the baseline window covers part of the response and the response window misses the response's maximum.

The correct timing of the baseline and response window depends very much on the actual triggering- and sound-display-hardware, it is therefore quite often necessary to shift both windows to fit your setup. This is no problem, as long as you keep these parameters constant over all your trials. To shift either the baseline or the response window, you can drag-and-drop the dotted lines on the graph to an optimal position:

When you drag and drop the response window or the basline window to an another position, the positions of these windows are updated in the baseline begin- and response begin-fields of the startle control section.

Note: if either the baseline window or the response window are adjusted, ANSLAB automatically recomputes the response onset and the response maximum based on the new window positions. ANSLAB also stores the window positions once the editing is confirmed (by clicking accept, invalid, zero, or inv. onset in the 'dynamic'-section). If you just switch to another trial (e.g. by using the buttons < or >) the window positions for the current trial are reset to the default positions. Hitting detect also resets the window positions to the default values.

The response onset latency is identified using the third graph, that shows a lowpass filtered rectified EMG signal. An onset is detected if the curve rises above a the mean value during the baseline interval plus a customizable number of standard deviations of the baseline signal. This threshold is illustrated by the horizontal red line in the third graph. You can adjust the threshold by using the x stddev of baseline field in the startle-control-section of the command window.

In the example below this threshold was clearly chosen too large, resulting in an overestimated onset time:

You can also modify the cutoff-frequency of the lowpass filter, which is applied to the onset detection signal, by changing the value in the lowpass cutoff [Hz]-field.

Once you have adjusted the windows and the detection parameters to the right values, you may save the values as default values to be used for all other trials and other files by clicking set (the values will then be saved to your 'anslabdef.m'-file in the study folder). In case of the response window and the baseline window the respective positions are then just set for the remaining, non-edited trials. To reset the window positions for an already edited trial to these default values, select the respective trial and hit detect.

Scoring startle trials

Use the buttons accept, invalid, inv. onset, and zero to jump from trial to trial and decide wether trials are valid or not. The different buttons have the following meanings:
Button Meaning
accept saves the current trial
invalid will set the parameters for the current trial to NaN (not a number)
inv. onset will set the parameters regarding the onset of the current trial to NaN (not a number)
zero will set the parameters for the current trial to zero

The extracted variables are displayed in the command window. This output will look something like this:
last edited: trial 1 : at 0.009321s : amplitude 21.8695 : baseline 1.9437 : onset latency 57ms : response latency 103ms : condition 196
pending approval : trial 2 : at 19.478s : amplitude 12.1778 : baseline 1.7035 : onset latency 79ms : response latency 112ms : condition 161
--------------------------------------------------------------------------
last edited: trial 2 : at 0.019477s : amplitude 12.1778 : baseline 1.7035 : onset latency 79ms : response latency 112ms : condition 161
pending approval : trial 3 : at 30.533s : amplitude 11.9867 : baseline 2.5102 : onset latency 82ms : response latency 109ms : condition 196
--------------------------------------------------------------------------
last edited: trial 3 : at 0.030532s : amplitude 11.9867 : baseline 2.5102 : onset latency 82ms : response latency 109ms : condition 196
pending approval : trial 4 : at 42.19s : amplitude 1.3704 : baseline 2.2463 : onset latency 49ms : response latency 53ms : condition 162
			
Most important of these values are startle response magnitude (amplitude = peak minus baseline value), startle response latency (from tone onset to peak response as evident in the EMG average upper window), and startle onset latency (from tone onset to onset of EMG response as evident in the upper raw EMG window).

These variables are illustrated by the red and blue dot in the startle response graph. If the automatically detected positions of these dots do not seem appropriate to you, you can drag and drop the dots to a position along the response line - latency and magnitude values will be updated according to the new positions.

Note: similar to the detection parameters, changed startle onset and maximum positions for a trial will only be saved once the trial settings have been confirmed (e.g. by hitting accept). Clicking detect will reset the position of the startle onset and maximum to automatically detected values.

Setting the startle response maximum

If there are two equally plausible peaks, pick the one closer to the normal response time for that subject and above the 5 SD line (see example 2 below: the first response is closer to this subject's normal response latency).

If there is no clear response, move the dot to a clearly identifiable peak that is approximately at the normal response time for that subject in order to not distort response latency. Do this only if the standard deviation is low (see example 3 below: this response might justifiably be set a little later on the second discernible peak if it reflects the subject's normal response latency; or alternatively, set to 0 response).

If the standard deviation is higher (i.e., much higher than any of the responses in that trial), consider excluding this response by hitting the button invalid, as this response measurement is probably not reliable due to an unstable baseline.

Analysis results display

After you have gone through one file trial-by-trial, startle magnitude and latencies are displayed for each response:

They appear as step functions, because ANSLAB is set to extrapolate from each response for the whole intertrial interval. Choose save reduced to save the response scores. Then, a mat-file and a text file are created in the startle subfolder of your study folder.

Text output of startle data

This is an example of a textfile written by ANSLAB after the analysis of each file:
Subject File FilePart Trial Time Amplitude Latency OnsetLatency BaselineAmplitude Condition Edited
104 1 0 1 9.321 218.695 103 57 19.437 196 1
104 1 0 2 19.477 121.778 112 79 17.035 161 1
104 1 0 3 30.532 119.867 109 82 25.102 196 1
104 1 0 4 42.189 13.704 53 49 22.463 162 1
104 1 0 5 53.345 96.164 84 77 26.845 146 1
104 1 0 6 64.991 72.945 84 54 22.864 139 1
104 1 0 7 75.136 5.815 108 55 16.944 193 1
104 1 0 8 87.282 35.449 92 -9999 29.387 147 1
104 1 0 9 98.438 111.194 124 101 23.102 135 1
104 1 0 10 108.585 47.101 114 94 27.185 145 1
104 1 0 11 120.761 166.228 99 60 29.543 163 1

The following table provides information on the columns of the text output (missing data is stored as -9999):
Column Meaning
Trial the trial number
Time timepoint of trial in datafile, given in seconds from file start
Amplitude startle magnitude (baseline corrected, so, the non-baseline corrected response amplitude can be obtained by adding BaselineAmplitude to Amplitude)
Latency startle response latency (given in milliseconds)
OnsetLatency onset of startle response (given in milliseconds)
BaselineAmplitude the baseline amplitude
Condition marker value recorded at the presentation of the startle tone or segment value
The variables you will want to use in general are Amplitude and Latency.

Ratio adjustment for reactivity scores (task minus baseline) may be more appropriate than change scores with EMG data. The startle response magnitude for a given individual depends on many factors: exact placement of the electrodes, muscle size, innervation density, skin thickness, etc. All these are probably multiplicative rather than additive factors for the response estimation. However, often additive change scores are used in publications.
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Editing examples


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