Difference: DocsSectionsExampleSpellerIntro (1 vs. 6)

Revision 606 Jan 2012 - Main.MarjoleinVanDerWaal

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META TOPICPARENT name="WebHome"

Example 2: Visual speller

Introduction

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Parts of this example

This example consists of five parts. In each consecutive step, we will use another feature of BrainStream to increase the performance of the speller.

Changed:
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  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
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  1. In the first part, we will build a simple speller. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
 
  1. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted.
Changed:
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  1. In the third part, we will direct the presentation of stimuli to a client.
  2. In the fourth part, a loop function will be used for stimulus presentation. This loop cannot be interrupted by BrainStream.
  3. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary.
>
>
  1. In the third part, we will direct the presentation of stimuli to a client.
  2. In the fourth part, a simple loop function will be used for stimulus presentation. This loop cannot be interrupted by BrainStream.
  3. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary.
  Continue with Part 1 ->

Revision 503 Jan 2012 - Main.MarjoleinVanDerWaal

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

Example 2: Visual speller

Introduction

Line: 27 to 27
  This example consists of five parts. In each consecutive step, we will use another feature of BrainStream to increase the performance of the speller.
  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
Changed:
<
<
  1. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted, for example so that numbers which are more likely to be the target will be flashed more often.
>
>
  1. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted.
 
  1. In the third part, we will direct the presentation of stimuli to a client.
  2. In the fourth part, a loop function will be used for stimulus presentation. This loop cannot be interrupted by BrainStream.
  3. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary.

Revision 421 Nov 2011 - Main.MarjoleinVanDerWaal

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

Example 2: Visual speller

Introduction

Line: 11 to 11
  The visual speller was first described by Farwell and Donchin in 1988 [1]. A visual speller consists of a matrix which usually contains all the letters of the alphabet.
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VisualSpeller.png
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VisualSpeller.png
  Figure 1: Visual speller
Line: 26 to 26
 

Parts of this example

This example consists of five parts. In each consecutive step, we will use another feature of BrainStream to increase the performance of the speller.

Changed:
<
<
  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
>
>
  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
 
  1. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted, for example so that numbers which are more likely to be the target will be flashed more often.
  2. In the third part, we will direct the presentation of stimuli to a client.
  3. In the fourth part, a loop function will be used for stimulus presentation. This loop cannot be interrupted by BrainStream.
  4. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary.
Added:
>
>
Continue with Part 1 ->
 
[1] Farwell, L.A. & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70, 510-523.

Revision 318 Nov 2011 - Main.MarjoleinVanDerWaal

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

Example 2: Visual speller

Introduction

Line: 11 to 11
  The visual speller was first described by Farwell and Donchin in 1988 [1]. A visual speller consists of a matrix which usually contains all the letters of the alphabet.
Changed:
<
<
VisualSpeller.png
>
>
VisualSpeller.png
  Figure 1: Visual speller

The rows and columns of this matrix are intensified in random order by briefly increasing the luminance of all elements within a row or column, which is perceived as a flash. Subjects are asked to direct their attention to one of the elements of the matrix. They can do this by counting how many times the target element flashes. As a result, the brain response to a flash of a row or column containing the target character is different from the brain response to other rows and columns. After a sequence of stimuli, in which all rows and columns are flashed several times, a classifier can be trained to distinguish between brain responses to attended and unattended characters. Thus, subjects are able to select elements from the matrix by directing their attention to this element.

The visual speller that we are going to build in this example will be a 3x3 matrix containing the numbers 1 to 9.

Added:
>
>
 

The experiment

Changed:
<
<
This visual speller experiment will consist of three parts. First, we must collect data which can be used to train the classifier. We will call this block the Train block. Subsequently, we must use the data we collected during the first block to train the classifier. The second block will be called the Classify block. Finally, we can use the classifier for actually selecting numbers. This is called the Test block. In this example, we will show how to create each of the blocks.
>
>
This visual speller experiment will consist of three parts. First, we must collect data which can be used to train the classifier. We will call this block the Train block. Subsequently, we must use the data we collected during the first block to train the classifier. The second block will be called the Classify block. Finally, we can use the classifier for actually selecting numbers. This is called the Test block. In this example, we will focus on the Train block.
 

Parts of this example

This example consists of five parts. In each consecutive step, we will use another feature of BrainStream to increase the performance of the speller.

Revision 218 Nov 2011 - Main.MarjoleinVanDerWaal

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META TOPICPARENT name="WebHome"
Changed:
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Example 2: Visual speller - Introduction

>
>

Example 2: Visual speller

Introduction

 
Changed:
<
<
In the following, we will show how to create a visual speller in BrainStream in five steps. In each consecutive step, we will use another feature of BrainStream to make the speller perform better.
>
>
In this example, we will show how to create a visual speller in BrainStream.
 

The visual speller

Changed:
<
<
The visual speller was first described by Farwell and Donchin (1988). A visual speller consists of a matrix which usually contains all the letters of the alphabet.
>
>
The visual speller was first described by Farwell and Donchin in 1988 [1]. A visual speller consists of a matrix which usually contains all the letters of the alphabet.
 
Changed:
<
<
[figure]
>
>
VisualSpeller.png
 
Changed:
<
<
The visual speller that we build in this example will be a 3x3 matrix containing the numbers 1 to 9. The rows and columns of this matrix are intensified in random order by briefly increasing the luminance of all elements within a row or column, which is perceived as a flash. Subjects are asked to direct their attention to one of the elements of the matrix. They can do this by counting how many times the target element flashes. As a result, the brain response to a flash of a row or column containing the target character is different from the brain response to other rows and columns. After a sequence of stimuli, in which each row and column are flashed several times, a classifier can be trained to distinguish between brain responses to attended and unattended characters. Thus, after a training phase, subjects are able to select elements from the matrix by directing their attention to this element. If the elements are letters, subjects can spell words. In our case, subjects will be able to select a sequence of numbers.
>
>
Figure 1: Visual speller

The rows and columns of this matrix are intensified in random order by briefly increasing the luminance of all elements within a row or column, which is perceived as a flash. Subjects are asked to direct their attention to one of the elements of the matrix. They can do this by counting how many times the target element flashes. As a result, the brain response to a flash of a row or column containing the target character is different from the brain response to other rows and columns. After a sequence of stimuli, in which all rows and columns are flashed several times, a classifier can be trained to distinguish between brain responses to attended and unattended characters. Thus, subjects are able to select elements from the matrix by directing their attention to this element.

The visual speller that we are going to build in this example will be a 3x3 matrix containing the numbers 1 to 9.

 

The experiment

Changed:
<
<
This visual speller experiment will consist of three parts. First, we must collect data which can be used to train the classifier. We will call this block the Train block. Subsequently, we must use the data we collected during the first block to train the classifier. Finally, we can use the classifier for actually selecting numbers. This is called the Test block. In this example, we will show how to create each of the blocks.
>
>
This visual speller experiment will consist of three parts. First, we must collect data which can be used to train the classifier. We will call this block the Train block. Subsequently, we must use the data we collected during the first block to train the classifier. The second block will be called the Classify block. Finally, we can use the classifier for actually selecting numbers. This is called the Test block. In this example, we will show how to create each of the blocks.
 

Parts of this example

Changed:
<
<
This example consists of five parts.
>
>
This example consists of five parts. In each consecutive step, we will use another feature of BrainStream to increase the performance of the speller.
 
  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
  2. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted, for example so that numbers which are more likely to be the target will be flashed more often.
  3. In the third part, we will direct the presentation of stimuli to a client.
Changed:
<
<
  1. In the fourth part, a loop will be specified for stimulus presentation. This loop cannot be interrupted by BrainStream.
  2. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary
>
>
  1. In the fourth part, a loop function will be used for stimulus presentation. This loop cannot be interrupted by BrainStream.
  2. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary.


[1] Farwell, L.A. & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70, 510-523.

META FILEATTACHMENT attachment="VisualSpeller.png" attr="" comment="" date="1321604179" name="VisualSpeller.png" path="VisualSpeller.png" size="17908" stream="VisualSpeller.png" tmpFilename="/var/tmp/CGItemp60691" user="MarjoleinVanDerWaal" version="1"

Revision 117 Nov 2011 - Main.MarjoleinVanDerWaal

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="WebHome"

Example 2: Visual speller - Introduction

In the following, we will show how to create a visual speller in BrainStream in five steps. In each consecutive step, we will use another feature of BrainStream to make the speller perform better.

The visual speller

The visual speller was first described by Farwell and Donchin (1988). A visual speller consists of a matrix which usually contains all the letters of the alphabet.

[figure]

The visual speller that we build in this example will be a 3x3 matrix containing the numbers 1 to 9. The rows and columns of this matrix are intensified in random order by briefly increasing the luminance of all elements within a row or column, which is perceived as a flash. Subjects are asked to direct their attention to one of the elements of the matrix. They can do this by counting how many times the target element flashes. As a result, the brain response to a flash of a row or column containing the target character is different from the brain response to other rows and columns. After a sequence of stimuli, in which each row and column are flashed several times, a classifier can be trained to distinguish between brain responses to attended and unattended characters. Thus, after a training phase, subjects are able to select elements from the matrix by directing their attention to this element. If the elements are letters, subjects can spell words. In our case, subjects will be able to select a sequence of numbers.

The experiment

This visual speller experiment will consist of three parts. First, we must collect data which can be used to train the classifier. We will call this block the Train block. Subsequently, we must use the data we collected during the first block to train the classifier. Finally, we can use the classifier for actually selecting numbers. This is called the Test block. In this example, we will show how to create each of the blocks.

Parts of this example

This example consists of five parts.

  1. In the first part, we will build a simple speller using only BrainStream. In this example, the stimulus sequence will be determined at the start of the experiment and cannot be changed later on.
  2. In the second part, we will create a dynamic stimulus sequence. This means that during the experiment, the stimulus sequence may be adapted, for example so that numbers which are more likely to be the target will be flashed more often.
  3. In the third part, we will direct the presentation of stimuli to a client.
  4. In the fourth part, a loop will be specified for stimulus presentation. This loop cannot be interrupted by BrainStream.
  5. In the final part, we will use a looptick function for stimulus presentation. This allows BrainStream to interrupt the stimulus presentation when necessary
 
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