Contributions:PAC - Revision history

Wengelhardt at 18:16, 2 May 2024

2024-05-02T18:16:48Z

← Older revision		Revision as of 18:16, 2 May 2024
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	==Signal Processing Pipeline==		==Signal Processing Pipeline==
	Below is the diagram ~~showing how this is made possible~~.		To allow for maximal flexibility, the pipeline is set up to compute Spectral Estimation in parallel with the phase-amplitude coupling computation. [[User Reference:SpectralEstimator\|The pipeline for the Spectral Estimation is already described here]].

			Below is the diagram describing the pipeline for phase-amplitude coupling.
	[[File:PAC Signal processing pipeline.png\|center]]		[[File:PAC Signal processing pipeline.png\|center]]
	===BCI2000 Signal Processing Pipeline===		===BCI2000 Signal Processing Pipeline===
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	#'''[[Contributions:HilbertFilter\|Hilbert filter]]:''' Compute the Hilbert filter on the bandpassed data.		#'''[[Contributions:HilbertFilter\|Hilbert filter]]:''' Compute the Hilbert filter on the bandpassed data.
	#'''PAC filter:''' Combines the data from the amplitude and phase and groups the output data by phase bins.		#'''PAC filter:''' Combines the data from the amplitude and phase and groups the output data by phase bins.
			#*Output: Z-score of amplitude for each phase, with the range [-π, π).
	#'''[[Programming Reference:SignalSharingDemo Signal Processing\|SignalSharingDemo]]:''' Enables an outside connection to the BCI2000 data by using shared memory. This is how we can see the data in Python.		#'''[[Programming Reference:SignalSharingDemo Signal Processing\|SignalSharingDemo]]:''' Enables an outside connection to the BCI2000 data by using shared memory. This is how we can see the data in Python.

Wengelhardt at 21:36, 5 April 2024

2024-04-05T21:36:55Z

@@ Line 13: / Line 13: @@
 ==Installation==
-#Python 3.7+ needs to be installed. [https://www.python.org/downloads/| You can download the lastest version here].
+See [[Python Visualizations#Installation|the Installation section on the Python Visualization page]] to see how to install the necessary packages and how to run demo files! Once that is all set up, you can return to this page to understand the specific parameters for the phase-amplitude coupling.
@@ Line 60: / Line 27: @@
 #'''PAC filter:''' Combines the data from the amplitude and phase and groups the output data by phase bins.
 #'''[[Programming Reference:SignalSharingDemo Signal Processing|SignalSharingDemo]]:''' Enables an outside connection to the BCI2000 data by using shared memory. This is how we can see the data in Python.
 ==Parameters==

Wengelhardt at 15:04, 18 March 2024

2024-03-18T15:04:42Z

← Older revision		Revision as of 15:04, 18 March 2024
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	\|Keeps track of the trial type.		\|Keeps track of the trial type.
	\|}		\|}
			==Offline Use==
			To run the Phase-Amplitude Coupling pipeline on data that has already been acquired, use the [[Contributions:FilePlayback\|FilePlayback]] module. This plays back the BCI2000 data file as if the data is currently being recorded. You can completely replicate the results of the visualization.


	==See also==		==See also==
	[[Contributions:SignalProcessing]]		[[Contributions:SignalProcessing\|SignalProcessing]] \| [[Contributions:FilePlayback\|FilePlayback]] \| [[Programming Reference:SignalSharing Python Demo\|SignalSharing Python Demo]]

	[[Category:Contributions]][[Category:~~Contributions~~:~~SignalProcessing~~]]		[[Category:Contributions]] [[Category:Filters]] [[Category:Signal Processing]] [[Category:User Interface]] [[Category:Data Analysis Tools]]

Wengelhardt at 19:57, 14 March 2024

2024-03-14T19:57:05Z

← Older revision		Revision as of 19:57, 14 March 2024
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	==Synopsis==		==Synopsis==
	PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes for a certain task.		PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes for a certain task.

			The inspiration for this visualization comes from Dr. Kai Miller's [https://pubmed.ncbi.nlm.nih.gov/22969416/\| ''Human motor cortical activity is selectively phase-entrained on underlying rhythms''] paper. Specifically, [https://pubmed.ncbi.nlm.nih.gov/22969416/#&gid=article-figures&pid=figure-5-uid-4\| Figure 5], shown below.
			[[File:Miller2012 fig5.jpg\|frame\|center\|See the official figure caption [https://pubmed.ncbi.nlm.nih.gov/22969416/#&gid=article-figures&pid=figure-5-uid-4\| here]]]
			One can see that Section C and D are reproduced with the Phase-Amplitude Coupling (PAC) module in real-time.

	==Versioning==		==Versioning==
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	==Installation==		==Installation==
	#Python 3.7+ needs to be installed. [[https://www.python.org/downloads/\| You can download the lastest version here]].		#Python 3.7+ needs to be installed. [https://www.python.org/downloads/\| You can download the lastest version here].
	#A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.		#A couple additional packages are used in this visualization. Run the following segments using <code>pip</code> in the command line.
	#*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>		#*PyQtGraph 0.13.3+: <code>pip install pyqtgraph</code>
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	==Signal Processing Pipeline==		==Signal Processing Pipeline==
	Below is the diagram showing how this is made possible.		Below is the diagram showing how this is made possible.
	[[File:PAC Signal processing pipeline.png]]		[[File:PAC Signal processing pipeline.png\|center]]
	===BCI2000 Signal Processing Pipeline===		===BCI2000 Signal Processing Pipeline===
	Within the BCI2000 architecture, the figure depicts the Signal Processing pipeline with the PAC Filter.		Within the BCI2000 architecture, the figure depicts the Signal Processing pipeline with the PAC Filter.

Wengelhardt at 19:38, 14 March 2024

2024-03-14T19:38:35Z

← Older revision		Revision as of 19:38, 14 March 2024
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			[[File:PAC Demo.gif\|thumb\|568px\|A demo of the Phase-Amplitude Coupling visualization]]

	==Synopsis==		==Synopsis==
	PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes for a certain task.		PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes for a certain task.

Wengelhardt at 18:36, 14 March 2024

2024-03-14T18:36:45Z

← Older revision		Revision as of 18:36, 14 March 2024
Line 61:		Line 61:

	==Parameters==		==Parameters==
			The PAC Module contains many parameters from the various filters in the Signal Processing Pipeline. Refer to those pages' documentation for specific on the individual parameters. There are additionally a couple of parameters specific to the PAC filter itself.

			{\|
			\|'''AngleDecimation'''
			\|Number of degrees of resolution. This inversely specifies the number of bins that the phases are grouped into. Must be a denominator of 360 degrees, otherwise it will be rounded to one. For example, a value of 10 will create 36 bins.
			\|-
			\|style="border-bottom: solid 1px grey; border-top: solid 1px grey;"\|'''SlidingWindow'''
			\|Amount of time to hold past data for. Set to 0 to hold for the whole run. Regardless of this parameter, when a New Trial occurs the data resets.
			\|-
			\|'''IncludeDeviation'''
			\|Enable to double the size of the output and include the standard deviation of the average amplitude for each phase bin. This will be the 2nd half of the output. If this is enabled, currently the Python script will have to be changed.
			\|}

			Since we have a parallel filter chain with equivalent subchains, we will have two subchains with the same parameter names. These will be included under the tabs ''Filtering: Amplitude'' and ''Filtering: Phase''. In these two subchains, a few requirements must be met.
			*Delay must be the same
			*Filtering: Amplitude must have the Hilbert OutputSignal of Hilbert envelope.
			*Filtering: Phase must have the OutputSignal of Hilbert phase.
			*Downsample must be disabled (since the two subchains will likely have varying frequencies, downsampling will mismatch the number of samples)
	==States==		==States==
			{\|
			\|'''PAC_TrialNumber'''
			\|Number that increments every time button New Trial is pressed. Changing this state resets all data.
			\|-
			\|'''PAC_TrialType'''
			\|Keeps track of the trial type.
			\|}

Wengelhardt at 18:11, 14 March 2024

2024-03-14T18:11:40Z

@@ Line 13: / Line 13: @@
 #*Numpy: <code>pip install numpy</code>
 #*SciPy: <code>pip install scipy</code> (only the stats package is required)
 ==Signal Processing Pipeline==

Wengelhardt at 17:53, 14 March 2024

2024-03-14T17:53:17Z

@@ Line 5: / Line 5: @@
 ===Authors===
 Will Engelhardt (engelhardt@neurotechcenter.org)
 ==Signal Processing Pipeline==
 [[File:PAC Signal processing pipeline.png]]
 ==Parameters==

Wengelhardt: Created page with "==Synopsis== PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes fo..."

2024-03-14T00:11:58Z

Created page with "==Synopsis== PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes fo..."

New page

==Synopsis==
PAC, or phase-amplitude coupling, shows the entrainment of a certain frequency band to the power of another frequency band. In other words, the phase of one type of neural activity correlates with the amplitude of another. The dominant phase and the location of this entrainment can change for varying tasks. With this implementation, you can view the entrainment happening as the task is being completed, which can help you focus on the responsive electrodes for a certain task.

==Versioning==
===Authors===
Will Engelhardt (engelhardt@neurotechcenter.org)

==Signal Processing Pipeline==
[[File:PAC Signal processing pipeline.png]]

==Parameters==

==States==

==See also==
[[Contributions:SignalProcessing]]

[[Category:Contributions]][[Category:Contributions:SignalProcessing]]