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JohnGriffiths committed Mar 7, 2024
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2 changes: 1 addition & 1 deletion .buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: 0b0845f06aa0ea4305158e51e2e10944
config: 94b4cc70cc05f65dc8cc2bd92a8c3d1e
tags: 645f666f9bcd5a90fca523b33c5a78b7
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# N170 run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run \nan experiment. \n"
"\n# N170 run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-expy, and how to run \nan experiment. \n"
]
},
{
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SSVEP run experiment
===============================
This example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run
This example demonstrates the initiation of an EEG stream with eeg-expy, and how to run
an experiment.
"""
@@ -40,4 +40,4 @@
# ---------------------
#
ssvep = VisualSSVEP(duration=record_duration, eeg=eeg_device, save_fn=save_fn)
ssvep.run()
ssvep.run()
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# SSVEP Visualization\n\nThis example demonstrates loading, organizing, and visualizing data from the steady-state visual evoked potentials (SSVEP) experiment. \n\nThe data used is the first subject and first session of the one of the eeg-notebooks ssvep example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figures show the visual frequencies appearing in the measured power spectrum. \n"
"\n# SSVEP Visualization\n\nThis example demonstrates loading, organizing, and visualizing data from the steady-state visual evoked potentials (SSVEP) experiment. \n\nThe data used is the first subject and first session of the one of the eeg-expy ssvep example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figures show the visual frequencies appearing in the measured power spectrum. \n"
]
},
{
@@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Load Data\n ---------------------\n\n We will use the eeg-notebooks SSVEP example dataset\n\n Note that if you are running this locally, the following cell will download\n the example dataset, if you do not already have it.\n\n##################################################################################################\n\n"
"Load Data\n ---------------------\n\n We will use the eeg-expy SSVEP example dataset\n\n Note that if you are running this locally, the following cell will download\n the example dataset, if you do not already have it.\n\n##################################################################################################\n\n"
]
},
{
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N170 run experiment
===============================
This example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run
This example demonstrates the initiation of an EEG stream with eeg-expy, and how to run
an experiment.
"""
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SSVEP Decoding
===============================
This notebook runs only the data analysis part of N170 notebook.
This notebook runs only the data analysis part of experiment.
Look at the notes to see how this can be run on the web with binder or google collab.
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4 changes: 2 additions & 2 deletions _downloads/89f5ff349033ec3c3d48bcfb2c3c3de2/01r__ssvep_viz.py
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This example demonstrates loading, organizing, and visualizing data from the steady-state visual evoked potentials (SSVEP) experiment.
The data used is the first subject and first session of the one of the eeg-notebooks ssvep example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording.
The data used is the first subject and first session of the one of the eeg-expy ssvep example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording.
We first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present
in the specified data directory, they will be quickly downloaded from the cloud.
@@ -38,7 +38,7 @@
# Load Data
# ---------------------
#
# We will use the eeg-notebooks SSVEP example dataset
# We will use the eeg-expy SSVEP example dataset
#
# Note that if you are running this locally, the following cell will download
# the example dataset, if you do not already have it.
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# N170 Load and Visualize Data\n\nThis example demonstrates loading, organizing, and visualizing ERP response data from the visual N170 experiment. \n\nImages of faces and houses are shown in a rapid serial visual presentation (RSVP) stream.\n\nThe data used is the first subject and first session of the one of the eeg-notebooks N170 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). \nThis session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figure plotted at the end shows the N170 response ERP waveform. \n"
"\n# N170 Load and Visualize Data\n\nThis example demonstrates loading, organizing, and visualizing ERP response data from the visual N170 experiment. \n\nImages of faces and houses are shown in a rapid serial visual presentation (RSVP) stream.\n\nThe data used is the first subject and first session of the one of the eeg-expy N170 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). \nThis session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figure plotted at the end shows the N170 response ERP waveform. \n"
]
},
{
@@ -29,7 +29,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data\n\nWe will use the eeg-notebooks N170 example dataset\n\nNote that if you are running this locally, the following cell will download\nthe example dataset, if you do not already have it.\n\n\n"
"## Load Data\n\nWe will use the eeg-expy N170 example dataset\n\nNote that if you are running this locally, the following cell will download\nthe example dataset, if you do not already have it.\n\n\n"
]
},
{
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@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# SSVEP Decoding\n\nThis notebook runs only the data analysis part of N170 notebook.\n\nLook at the notes to see how this can be run on the web with binder or google collab.\n\nAll of the additional notes are removed; only the code cells are kept.\n"
"\n# SSVEP Decoding\n\nThis notebook runs only the data analysis part of experiment.\n\nLook at the notes to see how this can be run on the web with binder or google collab.\n\nAll of the additional notes are removed; only the code cells are kept.\n"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# P300 Load and Visualize Data\n\nThis example demonstrates loading, organizing, and visualizing ERP response data from the visual P300 experiment. The experiment uses a visual oddball paradigm. Images of cats and dogs are shwn in a rapid serial visual presentation (RSVP) stream, with cats and dogs categorized respectively as 'targets' or 'non-targets', according to which has high or low probability of occurring, respectively. \n\nThe data used is the first subject and first session of the one of the eeg-notebooks P300 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figure plotted at the end shows the P300 response ERP waveform. \n"
"\n# P300 Load and Visualize Data\n\nThis example demonstrates loading, organizing, and visualizing ERP response data from the visual P300 experiment. The experiment uses a visual oddball paradigm. Images of cats and dogs are shwn in a rapid serial visual presentation (RSVP) stream, with cats and dogs categorized respectively as 'targets' or 'non-targets', according to which has high or low probability of occurring, respectively. \n\nThe data used is the first subject and first session of the one of the eeg-expy P300 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording. \n\nWe first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present \nin the specified data directory, they will be quickly downloaded from the cloud. \n\nAfter loading the data, we place it in an MNE `Epochs` object, and obtain the trial-averaged response. \n\nThe final figure plotted at the end shows the P300 response ERP waveform. \n"
]
},
{
@@ -29,7 +29,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Load Data\n ---------------------\n\n We will use the eeg-notebooks N170 example dataset\n\n Note that if you are running this locally, the following cell will download\n the example dataset, if you do not already have it.\n\n##################################################################################################\n\n"
"Load Data\n ---------------------\n\n We will use the eeg-expy N170 example dataset\n\n Note that if you are running this locally, the following cell will download\n the example dataset, if you do not already have it.\n\n##################################################################################################\n\n"
]
},
{
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@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# P300 run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run \nan experiment. \n"
"\n# P300 run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-expy, and how to run \nan experiment. \n"
]
},
{
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@@ -2,7 +2,7 @@
P300 run experiment
===============================
This example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run
This example demonstrates the initiation of an EEG stream with eeg-expy, and how to run
an experiment.
"""
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@@ -29,7 +29,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data\n\nWe will use the eeg-notebooks visual cueing example dataset\n\n\n"
"## Load Data\n\nWe will use the eeg-expy visual cueing example dataset\n\n\n"
]
},
{
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# Load Data
# ---------------------
#
# We will use the eeg-notebooks visual cueing example dataset
# We will use the eeg-expy visual cueing example dataset
#

eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data')
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n# SSVEP run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-notebooks, and how to run \nan experiment. \n"
"\n# SSVEP run experiment\n\nThis example demonstrates the initiation of an EEG stream with eeg-expy, and how to run \nan experiment. \n"
]
},
{
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4 changes: 2 additions & 2 deletions _downloads/db7df1055da643ad96640e6377001dbf/01r__n170_viz.py
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Images of faces and houses are shown in a rapid serial visual presentation (RSVP) stream.
The data used is the first subject and first session of the one of the eeg-notebooks N170 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model).
The data used is the first subject and first session of the one of the eeg-expy N170 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model).
This session consists of six two-minute blocks of continuous recording.
We first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present
@@ -42,7 +42,7 @@
# Load Data
# ---------------------
#
# We will use the eeg-notebooks N170 example dataset
# We will use the eeg-expy N170 example dataset
#
# Note that if you are running this locally, the following cell will download
# the example dataset, if you do not already have it.
4 changes: 2 additions & 2 deletions _downloads/e500d6e9072380c7b8d43c4a40d8e4f1/01r__p300_viz.py
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@@ -4,7 +4,7 @@
This example demonstrates loading, organizing, and visualizing ERP response data from the visual P300 experiment. The experiment uses a visual oddball paradigm. Images of cats and dogs are shwn in a rapid serial visual presentation (RSVP) stream, with cats and dogs categorized respectively as 'targets' or 'non-targets', according to which has high or low probability of occurring, respectively.
The data used is the first subject and first session of the one of the eeg-notebooks P300 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording.
The data used is the first subject and first session of the one of the eeg-expy P300 example datasets, recorded using the InteraXon MUSE EEG headset (2016 model). This session consists of six two-minute blocks of continuous recording.
We first use the `fetch_datasets` to obtain a list of filenames. If these files are not already present
in the specified data directory, they will be quickly downloaded from the cloud.
@@ -39,7 +39,7 @@
# Load Data
# ---------------------
#
# We will use the eeg-notebooks N170 example dataset
# We will use the eeg-expy N170 example dataset
#
# Note that if you are running this locally, the following cell will download
# the example dataset, if you do not already have it.
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