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bertrandlalobertrandlalo
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Clean implicit frustration demo to fit the repo standards
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‎implicit/frustration/.gitignore

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‎implicit/frustration/LICENSE

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‎implicit/frustration/README.md

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# Implicit frustration
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Implicit Frustration application based on [Neuroadaptive technology enables implicit cursor
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control based on medial prefrontal cortex activity](https://www.pnas.org/doi/abs/10.1073/pnas.1605155114).
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## Setting up
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### With fake or recorded data (no device)
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The default configuration uses real recorded data. You can also try sinusoidal waves by commenting out the `replay.yaml` graph in `main.yaml` and uncomment `sinus.yaml`.
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### Using a Muse device
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Uncomment the `muse.yaml` graph in `main.yaml` file, and comment out the other input graphs.
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### Using a Unicorn device
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Uncomment the `unicorn.yaml` graph in `main.yaml` file, and comment out the other input graphs.
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## Running the demo
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Launch the app:
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```
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$ timeflux -d implicit/frustration/main.yaml
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```
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Open a browser, and visualize:
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- [The raw EEG data](http://localhost:8000/monitor/)
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- [The application to perform the experiment](http://localhost:8000/frustration/)
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![demo-bands-eeg](img/interface.gif)
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The application is already implemented for two eeg headsets:
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- the muse 2016 BLED by Muse in LSL
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- stream the LSL using muse_stream.sh

‎implicit/frustration/graphs/muse.yaml

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graphs:
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- id: muse
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nodes:
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- id: eeg
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module: timeflux.nodes.lsl
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class: Receive
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params:
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prop: type
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value: EEG
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unit: s
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sync: None
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- id: pub_eeg
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module: timeflux.nodes.zmq
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class: Pub
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params:
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topic: eeg
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- id: reindex
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module: timeflux.nodes.dejitter
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class: Reindex
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params:
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rate: 256
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edges:
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- source: eeg
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target: reindex
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- source: reindex
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target: pub_eeg
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rate: 5

‎implicit/frustration/graphs/replay.yaml

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graphs:
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- id: replay
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nodes:
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- id: replay
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module: timeflux.nodes.hdf5
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class: Replay
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params:
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filename: data/XXX.hdf5
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keys:
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- /eeg
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speed: 1
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- id: select
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module: timeflux.nodes.query
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class: LocQuery
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params:
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key: [ Fpz ]
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- id: pub
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module: timeflux.nodes.zmq
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class: Pub
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params:
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topic: eeg
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edges:
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- source: replay:eeg
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target: select
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- source: select
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target: pub
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rate: 10

‎implicit/frustration/graphs/save.yaml

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graphs:
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- id: Record
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nodes:
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- id: sub
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module: timeflux.nodes.zmq
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class: Sub
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params:
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topics: [events, eeg]
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- id: save
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module: timeflux.nodes.hdf5
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class: Save
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params:
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path: data
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complevel: 0
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min_itemsize: 200
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- id: debug
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module: timeflux.nodes.debug
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class: Display
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edges:
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- source: sub:*
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target: save
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- source: sub:events
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target: debug
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rate: 1

‎implicit/frustration/graphs/sinus.yaml

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graphs:
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- id: generate
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nodes:
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- id: sinus
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module: timeflux_audio.nodes.signal
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class: Additive
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params:
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frequencies: [1, 5, 10, 18, 30, 50] # center of delta, theta, alpha, beta, gamma bands and line noise
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resolution: 500
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amplitudes: [1, .5, .25, .12, .06, 0.2] # Amplitudes of bands and line noise
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name: Fpz
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# Publish
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# -------
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- id: pub
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module: timeflux.nodes.zmq
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class: Pub
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params:
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topic: eeg
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edges:
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- source: sinus
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target: pub
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rate: 10

‎implicit/frustration/graphs/unicorn.yaml

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graphs:
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- id: unicorn
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nodes:
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- id: eeg
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module: timeflux_brainflow.nodes.driver
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class: BrainFlow
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params:
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board: unicorn
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- id: pub_eeg
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module: timeflux.nodes.zmq
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class: Pub
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params:
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topic: eeg
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- id: reindex
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module: timeflux.nodes.dejitter
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class: Reindex
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params:
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rate: 250
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edges:
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- source: eeg
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target: reindex
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- source: reindex
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target: pub_eeg
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rate: 5

‎implicit/frustration/img/interface.gif

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‎implicit/frustration/main.yaml

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import:
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# Input graphs
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# -------------
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# - graphs/unicorn.yaml # uncomment this line to use unicorn device
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# - graphs/muse.yaml # uncomment this line to use muse device
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# - graphs/replay.yaml # uncomment this line to use replayed data
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- graphs/sinus.yaml # uncomment this line to use generated data
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# - graphs/save.yaml # uncomment this graph to save the data in HDF5
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graphs:
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- id: Broker
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nodes:
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- id: proxy
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module: timeflux.nodes.zmq
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class: Broker
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- id: UI
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nodes:
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- id: ui
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module: timeflux_ui.nodes.ui
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class: UI
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params:
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routes:
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frustration: www # app will be available at http://localhost:8000/frustration/
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settings:
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grid:
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grid:
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rows: 4
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columns: 4
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frustration:
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durations:
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step: 1000
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new_grid: 2000
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keys:
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activate: true
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good: b
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bad: v
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sessions: 30
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max_step: 15
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- id: sub
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module: timeflux.nodes.zmq
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class: Sub
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params:
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topics: [model, eeg]
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- id: pub
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module: timeflux.nodes.zmq
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class: Pub
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params:
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topic: events
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- id: bp_filter
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module: timeflux_dsp.nodes.filters
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class: IIRFilter
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params:
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order: 2
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frequencies: [0.1, 40]
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edges:
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- source: ui:events
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target: pub
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- source: sub:model
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target: ui:model
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- source: sub:eeg
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target: ui:eeg
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- source: sub:eeg
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target: bp_filter
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- source: bp_filter
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target: ui:filtered_eeg

‎implicit/frustration/muse.yaml

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