User Reference:Normalizer - Revision history

Mellinger: /* States */

2025-03-31T21:27:22Z

States

← Older revision		Revision as of 21:27, 31 March 2025
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	==States==		==States==
	Buffer condition expressions, and the UpdateTrigger expression may involve any [[BCI2000 Glossary#State\|state]] present in the system. Expressions are checked for syntactical correctness and whether referred~~-to~~ states are present during the [[Technical Reference:States of Operation\|Preflight Phase]].		Buffer condition expressions, and the UpdateTrigger expression may involve any [[BCI2000 Glossary#State\|state]] present in the system. Expressions are checked for syntactical correctness and whether referred states are present during the [[Technical Reference:States of Operation\|Preflight Phase]].

			===NormalizerOffset<1..3>===
			Value of Normalizer offset for the respective input/output channel. The value is stored as a float value and needs to be typecast to a 'single' when reading it from Matlab:
			[signal, states, parameters] = load_bcidat('testS001R01.dat');
			NormalizerGain1 = typecast(states.NormalizerGain1, 'single');

			===NormalizerGain<1..3>===
			Value of Normalizer gain for the respective input/output channel. The value is stored as a float value and needs to be typecast to a 'single' when reading it from Matlab.

	==Examples==		==Examples==

Mellinger: /* Adaptation Delay */

2009-06-05T13:26:10Z

Adaptation Delay

← Older revision		Revision as of 13:26, 5 June 2009
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	===Adaptation Delay===		===Adaptation Delay===
	One may suggest that the adaptation time may affect the accuracy of the cursor movement in mu rhythm experiments. However, the ~~following~~ reference shows that computing the normalization parameters either during or at the end of the trials leaded to similar results. Hence, in the current version of BCI2000, adaptation of the signal mean is accomplished at the end of each trial and using a Moving Average Algorithm.		One may suggest that the adaptation time may affect the accuracy of the cursor movement in mu rhythm experiments. However, the reference below shows that computing the normalization parameters either during or at the end of the trials leaded to similar results. Hence, in the current version of BCI2000, adaptation of the signal mean is accomplished at the end of each trial and using a Moving Average Algorithm.

	Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.		Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.

Mellinger: /* Adaptation Delay */

2009-06-05T13:24:37Z

Adaptation Delay

← Older revision		Revision as of 13:24, 5 June 2009
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	Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.		Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.

	:'''From the ~~Article~~ abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. ... Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.		:'''From the article abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. ... Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.

	===Adaptation Rationale===		===Adaptation Rationale===

Mellinger: /* Adaptation Delay */

2009-06-05T13:24:18Z

Adaptation Delay

← Older revision		Revision as of 13:24, 5 June 2009
Line 20:		Line 20:
	Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.		Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.

	:'''Article abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. ~~For all subjects combined, the five methods performed similarly~~. ~~However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods~~. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.		:'''From the Article abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. ... Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.

	===Adaptation Rationale===		===Adaptation Rationale===

Mellinger: /* Trial-based 1D Feedback Task with 3 Targets */

2009-06-05T12:34:48Z

Trial-based 1D Feedback Task with 3 Targets

← Older revision		Revision as of 12:34, 5 June 2009
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	Adaptation= 0		Adaptation= 0
	NormalizerOffset= -1		NormalizerOffset= -1
	NormalizerGain= 1 ~~or 2~~, respectively.		NormalizerGain= 2 or 1, respectively.

	===Trial-based 2D Feedback Task with 4 Targets===		===Trial-based 2D Feedback Task with 4 Targets===

Mellinger: /* Trial-based 1D Feedback Task with 3 Targets */

2009-06-05T12:32:42Z

Trial-based 1D Feedback Task with 3 Targets

← Older revision		Revision as of 12:32, 5 June 2009
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	===Trial-based 1D Feedback Task with 3 Targets===		===Trial-based 1D Feedback Task with 3 Targets===
	*Only data from the feedback phase should enter into the adaptation.		*Only data from the feedback phase should enter into the adaptation.
	*To make sure that targets contribute equally to the adaptation, we use a single buffer for each target. We use a 3-rows-by-1-column BufferConditions (see below). Depending on which control signal should be adapted on, these conditions need to be entered in the first, second, or third column of the matrix (corresponding to x, y, and z direction when used with the [[User Reference:CursorTask\|CursorTask]] application module).		*To make sure that targets contribute equally to the adaptation, we use a single buffer for each target. We use a 3-rows-by-1-column ''BufferConditions'' matrix (see below). Depending on which control signal should be adapted on, all of these conditions need to be entered in the first, second, or third column of the matrix (corresponding to x, y, and z direction when used with the [[User Reference:CursorTask\|CursorTask]] application module).
	(Feedback)&&(TargetCode==1)		(Feedback)&&(TargetCode==1)
	(Feedback)&&(TargetCode==2)		(Feedback)&&(TargetCode==2)

Mellinger: /* NormalizerOffsets, NormalizerGains */

2009-06-05T12:30:35Z

NormalizerOffsets, NormalizerGains

← Older revision		Revision as of 12:30, 5 June 2009
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	===NormalizerOffsets, NormalizerGains===		===NormalizerOffsets, NormalizerGains===
	Lists of offset and gain values, with entries corresponding to signal channels. These values will be updated depending on the channel's adaptation configuration in the Adaptation parameter.		Lists of offset and gain values, with entries corresponding to signal channels. These values will be updated depending on the channel's adaptation configuration in the ''Adaptation'' parameter.

	===Adaptation===		===Adaptation===
	A list of values that determines adaptation strategy individually for each channel. Possible values are		A list of values that determines adaptation strategy individually for each channel. Possible values are

Mellinger: /* Adaptation Delay */

2009-06-05T12:29:10Z

Adaptation Delay

← Older revision		Revision as of 12:29, 5 June 2009
Line 20:		Line 20:
	Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.		Ramoser H, Wolpaw JR, Pfurtscheller G, "EEG-based communication: evaluation of alternative signal prediction methods". Biomed Tech (Berl); 42(9) : 226-33, Sep, 1997.

	'''Article abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.		:'''Article abstract'''-Individuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.

	===Adaptation Rationale===		===Adaptation Rationale===

Mellinger: /* Adaptation */

2009-06-05T12:26:53Z

Adaptation

← Older revision		Revision as of 12:26, 5 June 2009
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	Each data buffer is associated with such a boolean expression. Whenever an expression evaluates to ''true'', the current input will be appended to the associated buffer.		Each data buffer is associated with such a boolean expression. Whenever an expression evaluates to ''true'', the current input will be appended to the associated buffer.
	Whenever it comes to updating offset and gain values, the Normalizer will use the content of its buffers to estimate data mean and variance. The offset will then be set to the data mean, and the gain to the inverse square root of the data variance, i.e., the inverse of the data standard deviation.		Whenever it comes to updating offset and gain values, the Normalizer will use the content of its buffers to estimate data mean and variance. The offset will then be set to the data mean, and the gain to the inverse square root of the data variance, i.e., the inverse of the data standard deviation.


	===Adaptation Delay===		===Adaptation Delay===

Mellinger: /* Adaptation */

2009-06-05T12:26:25Z

Adaptation

← Older revision		Revision as of 12:26, 5 June 2009
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	Whenever it comes to updating offset and gain values, the Normalizer will use the content of its buffers to estimate data mean and variance. The offset will then be set to the data mean, and the gain to the inverse square root of the data variance, i.e., the inverse of the data standard deviation.		Whenever it comes to updating offset and gain values, the Normalizer will use the content of its buffers to estimate data mean and variance. The offset will then be set to the data mean, and the gain to the inverse square root of the data variance, i.e., the inverse of the data standard deviation.


			===Adaptation Delay===
	One may suggest that the adaptation time may affect the accuracy of the cursor movement in mu rhythm experiments. However, the following reference shows that computing the normalization parameters either during or at the end of the trials leaded to similar results. Hence, in the current version of BCI2000, adaptation of the signal mean is accomplished at the end of each trial and using a Moving Average Algorithm.		One may suggest that the adaptation time may affect the accuracy of the cursor movement in mu rhythm experiments. However, the following reference shows that computing the normalization parameters either during or at the end of the trials leaded to similar results. Hence, in the current version of BCI2000, adaptation of the signal mean is accomplished at the end of each trial and using a Moving Average Algorithm.