Difference between revisions of "Template:StimulusTaskParams"

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(EarlyOffsetExpression)
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Switches result display of copy/free spelling on or off.
 
Switches result display of copy/free spelling on or off.
 
In the P3Speller, setting ''DisplayResults'' to 'off' will disable execution of all speller commands (such as switching matrices) as well.
 
In the P3Speller, setting ''DisplayResults'' to 'off' will disable execution of all speller commands (such as switching matrices) as well.
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====MinimumEvidence====
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By default, target selection is performed without considering the actual amount of evidence that favors the selected target over other targets. Although the selected target will always be a target with maximum classification score (i.e., evidence), other targets may have the same or a similar score. It may be useful to omit classification in such situations altogether, by specifying a minimum amount of evidence that must exist in favor of the selected target, when compared to the next-best target. When used together with the ''AccumulateEvidence'' option, this allows to dynamically control the number of stimulus presentations, by simply repeating stimulus sequences until a sufficient amount of evidence has been collected.
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Setting ''MinimumEvidence'' to 0 or to a negative number will result in default behavior, i.e. there will be a target selection each time classification scores are received from the SignalProcessing module. For values greater 0, the amount of selection errors will become smaller as the value of ''MinimumEvidence'' is increased; this increases the amount of information contained in each selection. At the same time, it becomes more and more unlikely that a selection will occur at all within a certain amount of time; this decreases the amount of information transmitted per time (information flow, or bitrate). In between, a certain value will correspond to an optimal compromise between selection errors, and selection duration. At this point, the flow of information is maximized.
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The meaning of the actual number entered into the ''MinimumEvidence'' parameter is relative to the amount of within-class variance present in the classification score. An evidence of 0 means a 50:50 chance for correct classification. Increasing the evidence value by two standard deviations corresponds to an improvement by a factor of roughly 88:12, four standard deviations correspond to (88:12)^2=(98:2) ... etc, approaching perfect classification as evidence increases towards infinity.
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You may use the "Normalize Within-Class Variance" option of P300Classifier in order to obtain classifier weights normalized to unit within-class variance. In this case, you may use the following equations to convert between the ''MinimumEvidence'' parameter <math>\eta</math>, and the correct classification chance <math>chi</math>:
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<math>\chi = \frac{50}{50} \left(\frac{88}{12}\right)^{\eta/2}</math>
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or, the same equation, solved for the evidence value:
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<math>\eta=2\frac{\log\chi}{\log(88/12)}</math>.

Revision as of 16:56, 18 March 2013

WindowBackgroundColor

The window's background color, given as an RGB value. For convenience, RGB values may be entered in hexadecimal notation, e.g. 0xff0000 for red.

PreRunDuration

The duration of the pause preceding the first sequence. Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar.

PostRunDuration

Duration of the pause following last sequence. Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar.

PreSequenceDuration

Duration of the pause preceding sequences (or sets of intensifications). Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar.

In free or copy mode, the PreSequenceDuration and PostSequenceDuration parameters may not go below twice the value of the StimulusDuration parameters, in order to allow for presentation of FocusOn and Result announcement stimuli.

PostSequenceDuration

Duration of the pause following sequences (or sets of intensifications). Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar.

When used in conjunction with the P3TemporalFilter, this value needs to be larger than the EpochLength parameter. This allows classification to complete before the next sequence of stimuli is presented.

StimulusDuration

For visual stimuli, the duration of stimulus presentation. For auditory stimuli, the maximum duration, i.e. playback of audio extending above the specified duration will be muted. Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar.

EarlyOffsetExpression

Allows the specification of an Expression that is constantly monitored during stimulus presentation. When the value of the Expression transitions from zero to non-zero, the stimulus is aborted early, even if the StimulusDuration has not yet elapsed. For example, set this Expression to KeyDown==32 and start your source module with the --LogKeyboard=1 flag: then the subject will be able to advance the stimulus sequence manually by pressing the space key.

ISIMinDuration, ISIMaxDuration

Minimum and maximum duration of the inter-stimulus interval. During the inter-stimulus interval, the screen is blank, and audio is muted.

Actual inter-stimulus intervals vary randomly between minimum and maximum value, with uniform probability for all intermediate values. Given in sample blocks, or in time units when immediately followed with 's', 'ms', or similar. Note that temporal resolution is limited to a single sample block.

InterpretMode

An enumerated value selecting on-line classification of evoked responses:

  • 0: no target is announced "attended", and no classification is performed;
  • 1: online or free mode: classification is performed, but no "attended target" is defined;
  • 2: copy mode: "attended" targets are defined, classification is performed.

DisplayResults

Switches result display of copy/free spelling on or off. In the P3Speller, setting DisplayResults to 'off' will disable execution of all speller commands (such as switching matrices) as well.

MinimumEvidence

By default, target selection is performed without considering the actual amount of evidence that favors the selected target over other targets. Although the selected target will always be a target with maximum classification score (i.e., evidence), other targets may have the same or a similar score. It may be useful to omit classification in such situations altogether, by specifying a minimum amount of evidence that must exist in favor of the selected target, when compared to the next-best target. When used together with the AccumulateEvidence option, this allows to dynamically control the number of stimulus presentations, by simply repeating stimulus sequences until a sufficient amount of evidence has been collected.

Setting MinimumEvidence to 0 or to a negative number will result in default behavior, i.e. there will be a target selection each time classification scores are received from the SignalProcessing module. For values greater 0, the amount of selection errors will become smaller as the value of MinimumEvidence is increased; this increases the amount of information contained in each selection. At the same time, it becomes more and more unlikely that a selection will occur at all within a certain amount of time; this decreases the amount of information transmitted per time (information flow, or bitrate). In between, a certain value will correspond to an optimal compromise between selection errors, and selection duration. At this point, the flow of information is maximized.

The meaning of the actual number entered into the MinimumEvidence parameter is relative to the amount of within-class variance present in the classification score. An evidence of 0 means a 50:50 chance for correct classification. Increasing the evidence value by two standard deviations corresponds to an improvement by a factor of roughly 88:12, four standard deviations correspond to (88:12)^2=(98:2) ... etc, approaching perfect classification as evidence increases towards infinity.

You may use the "Normalize Within-Class Variance" option of P300Classifier in order to obtain classifier weights normalized to unit within-class variance. In this case, you may use the following equations to convert between the MinimumEvidence parameter \eta, and the correct classification chance chi: \chi = \frac{50}{50} \left(\frac{88}{12}\right)^{\eta/2} or, the same equation, solved for the evidence value: \eta=2\frac{\log\chi}{\log(88/12)}.