deploy: 1257c3a

.buildinfo

@@ -1,4 +1,4 @@
 # 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: 75670e2e8c59814fc976e9ef5fbe74ea
+config: 0b0845f06aa0ea4305158e51e2e10944
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/18e4b69d6ddc9302425e645c1fec16c3/04r__cueing_group_analysis_winter2019.py

@@ -22,7 +22,7 @@
 from mne.time_frequency import tfr_morlet
 
 # EEG-Noteooks functions
-from eegnb.analysis.utils import load_data,plot_conditions
+from eegnb.analysis.utils import load_data
 from eegnb.datasets import fetch_dataset
 
 # sphinx_gallery_thumbnail_number = 1
@@ -136,14 +136,14 @@
     # Left Cue
     tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, 
                           n_cycles=wave_cycles, return_itc=True)
-    tfr = tfr.apply_baseline([-1,-.5],mode='mean')
+    tfr = tfr.apply_baseline((-1,-.5),mode='mean')
     power_Ipsi_TP9 = tfr.data[0,:,:]
     power_Contra_TP10 = tfr.data[1,:,:]
 
     # Right Cue
     tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, 
                           n_cycles=wave_cycles, return_itc=True)
-    tfr = tfr.apply_baseline([-1,-.5],mode='mean')
+    tfr = tfr.apply_baseline((-1,-.5),mode='mean')
     power_Contra_TP9 = tfr.data[0,:,:]
     power_Ipsi_TP10 = tfr.data[1,:,:]
 

_downloads/1f56a155b1a68a196eeb8aea2e0b7517/02r__cueing_group_analysis.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -80,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n        202, 203, 204, 205, 207, 208, 209, 210, 211, \n        301, 302, 303, 304, 305, 306, 307, 308, 309]\n\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies =  np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0    \n    \n    \n    \nfor sub in subs:\n    print(sub)\n    \n    sub_count += 1\n\n    \n    if (sub_count in really_bad_subs):\n        rej_thresh_uV = 90\n    elif (sub_count in bad_subs):\n        rej_thresh_uV = 90\n    else:\n        rej_thresh_uV = 90\n\n    rej_thresh = rej_thresh_uV*1e-6\n    \n    \n    # Load both sessions\n    raw = load_data(sub,1, # subject, session\n                    experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n                    data_dir = eegnb_data_path)\n                \n    raw.append(\n          load_data(sub,2, # subject, session\n                    experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n                    data_dir = eegnb_data_path))\n    \n\n    # Filter Raw Data\n    raw.filter(1,30, method='iir')\n\n    #Select Events\n    events = find_events(raw)\n    event_id = {'LeftCue': 1, 'RightCue': 2}\n    epochs = Epochs(raw, events=events, event_id=event_id, \n                    tmin=-1, tmax=2, baseline=(-1, 0), \n                    reject={'eeg':rej_thresh}, preload=True,\n                    verbose=False, picks=[0, 3])\n    print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n    # Compute morlet wavelet\n\n    # Left Cue\n    tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n                          n_cycles=wave_cycles, return_itc=True)\n    tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n    #tfr.plot(picks=[0], mode='logratio', \n    #         title='TP9 - Ipsi');\n    #tfr.plot(picks=[3], mode='logratio', \n    #         title='TP10 - Contra');\n    power_Ipsi_TP9 = tfr.data[0,:,:]\n    power_Contra_TP10 = tfr.data[1,:,:]\n\n    # Right Cue\n    tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n                          n_cycles=wave_cycles, return_itc=True)\n    tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n    #tfr.plot(picks=[0], mode='logratio', \n    #         title='TP9 - Contra');\n    #tfr.plot(picks=[3], mode='logratio', \n    #         title='TP10 - Ipsi');\n    power_Contra_TP9 = tfr.data[0,:,:]\n    power_Ipsi_TP10 = tfr.data[1,:,:]\n\n    # Plot Differences\n    #%matplotlib inline\n    times = epochs.times\n    power_Avg_Ipsi =   (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n    power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n    power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n\n\n    #find max to make color range\n    plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])\n    plot_diff_max = np.max(np.abs(power_Avg_Diff))\n\n   \n    \n    #Ipsi\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Ipsi,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title('Power Average Ipsilateral to Cue')\n    cb = fig.colorbar(im)\n    cb.set_label('Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n\n    #TP10\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Contra,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title(str(sub) + ' - Power Average Contra to Cue')\n    cb = fig.colorbar(im)\n    cb.set_label('Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n\n    #difference between conditions\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Diff,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title('Power Difference Ipsi-Contra')\n    cb = fig.colorbar(im)\n    cb.set_label('Ipsi-Contra Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n     \n        \n   \n        \n    #output data into array\n    Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n                                           np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                           )\n                   )            \n    Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n                                                   np.argmax(times>t_high)-1 ],1\n                                   )\n                           )\n        \n    Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n                                               np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                             )\n                     )\n    \n    Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n                                                       np.argmax(times>t_high)-1 ],1))\n    \n    \n    diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n                                           np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                           )\n                   )\n    diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n                                                   np.argmax(times>t_high)-1 ],1\n                                   )\n                           )\n    \n    \n    ERSP_diff_out.append(power_Avg_Diff)\n    ERSP_Ipsi_out.append(power_Avg_Ipsi)\n    ERSP_Contra_out.append(power_Avg_Contra)\n\n\n    \nprint(np.shape(ERSP_diff_out))\nprint(np.shape(Contra_spectra_out))\n\nprint(diff_out)"
+        "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n        202, 203, 204, 205, 207, 208, 209, 210, 211, \n        301, 302, 303, 304, 305, 306, 307, 308, 309]\n\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies =  np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0    \n    \n    \n    \nfor sub in subs:\n    print(sub)\n    \n    sub_count += 1\n\n    \n    if (sub_count in really_bad_subs):\n        rej_thresh_uV = 90\n    elif (sub_count in bad_subs):\n        rej_thresh_uV = 90\n    else:\n        rej_thresh_uV = 90\n\n    rej_thresh = rej_thresh_uV*1e-6\n    \n    \n    # Load both sessions\n    raw = load_data(sub,1, # subject, session\n                    experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n                    data_dir = eegnb_data_path)\n                \n    raw.append(\n          load_data(sub,2, # subject, session\n                    experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n                    data_dir = eegnb_data_path))\n    \n\n    # Filter Raw Data\n    raw.filter(1,30, method='iir')\n\n    #Select Events\n    events = find_events(raw)\n    event_id = {'LeftCue': 1, 'RightCue': 2}\n    epochs = Epochs(raw, events=events, event_id=event_id, \n                    tmin=-1, tmax=2, baseline=(-1, 0), \n                    reject={'eeg':rej_thresh}, preload=True,\n                    verbose=False, picks=[0, 3])\n    print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n    # Compute morlet wavelet\n\n    # Left Cue\n    tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n                          n_cycles=wave_cycles, return_itc=True)\n    tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n    #tfr.plot(picks=[0], mode='logratio', \n    #         title='TP9 - Ipsi');\n    #tfr.plot(picks=[3], mode='logratio', \n    #         title='TP10 - Contra');\n    power_Ipsi_TP9 = tfr.data[0,:,:]\n    power_Contra_TP10 = tfr.data[1,:,:]\n\n    # Right Cue\n    tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n                          n_cycles=wave_cycles, return_itc=True)\n    tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n    #tfr.plot(picks=[0], mode='logratio', \n    #         title='TP9 - Contra');\n    #tfr.plot(picks=[3], mode='logratio', \n    #         title='TP10 - Ipsi');\n    power_Contra_TP9 = tfr.data[0,:,:]\n    power_Ipsi_TP10 = tfr.data[1,:,:]\n\n    # Plot Differences\n    #%matplotlib inline\n    times = epochs.times\n    power_Avg_Ipsi =   (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n    power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n    power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n\n\n    #find max to make color range\n    plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])\n    plot_diff_max = np.max(np.abs(power_Avg_Diff))\n\n   \n    \n    #Ipsi\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Ipsi,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title('Power Average Ipsilateral to Cue')\n    cb = fig.colorbar(im)\n    cb.set_label('Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n\n    #TP10\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Contra,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title(str(sub) + ' - Power Average Contra to Cue')\n    cb = fig.colorbar(im)\n    cb.set_label('Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n\n    #difference between conditions\n    fig, ax = plt.subplots(1)\n    im = plt.imshow(power_Avg_Diff,\n               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)\n    plt.xlabel('Time (sec)')\n    plt.ylabel('Frequency (Hz)')\n    plt.title('Power Difference Ipsi-Contra')\n    cb = fig.colorbar(im)\n    cb.set_label('Ipsi-Contra Power')\n    # Create a Rectangle patch\n    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n    # Add the patch to the Axes\n    ax.add_patch(rect)\n     \n        \n   \n        \n    #output data into array\n    Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n                                           np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                           )\n                   )            \n    Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n                                                   np.argmax(times>t_high)-1 ],1\n                                   )\n                           )\n        \n    Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n                                               np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                             )\n                     )\n    \n    Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n                                                       np.argmax(times>t_high)-1 ],1))\n    \n    \n    diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n                                           np.argmax(frequencies>f_high)-1,\n                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n                           )\n                   )\n    diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n                                                   np.argmax(times>t_high)-1 ],1\n                                   )\n                           )\n    \n    \n    ERSP_diff_out.append(power_Avg_Diff)\n    ERSP_Ipsi_out.append(power_Avg_Ipsi)\n    ERSP_Contra_out.append(power_Avg_Contra)\n\n\n    \nprint(np.shape(ERSP_diff_out))\nprint(np.shape(Contra_spectra_out))\n\nprint(diff_out)"
       ]
     },
     {
@@ -172,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.8.18"
     }
   },
   "nbformat": 4,

_downloads/2f1a411d3414306e436c6540c86910c5/00x__n170_run_experiment.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -33,25 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments.visual_n170 import n170\n\n# Define some variables\nboard_name = \"muse\"\nexperiment = \"visual_n170\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initiate EEG device\n\nStart EEG device\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "eeg_device = EEG(device=board_name)\n\n# Create save file name\nsave_fn = generate_save_fn(board_name, experiment, subject_id, session_nb)\nprint(save_fn)"
+        "from eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments import VisualN170\n\n# Define some variables\nboard_name = \"muse2\" # board name\nexperiment_name = \"visual_n170\" # experiment name\nsubject_id = 0 # test subject id\nsession_nb = 0 # session number\nrecord_duration = 120 # recording duration\n\n# generate save path\nsave_fn = generate_save_fn(board_name, experiment_name, subject_id, session_nb)\n\n# create device object\neeg_device = EEG(device=board_name)\n\n# Experiment type\nexperiment = VisualN170(duration=record_duration, eeg=eeg_device, save_fn=save_fn)"
       ]
     },
     {
@@ -69,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "n170.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn)"
+        "experiment.run()\n\n# Saved csv location\nprint(\"Recording saved in\", experiment.save_fn)"
       ]
     }
   ],
@@ -89,7 +60,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.8.18"
     }
   },
   "nbformat": 4,

_downloads/335ce423c80436163e4a75b13e3dba64/00x__ssvep_run_experiment.py

@@ -15,10 +15,10 @@
 import os
 from eegnb import generate_save_fn
 from eegnb.devices.eeg import EEG
-from eegnb.experiments.visual_ssvep import ssvep
+from eegnb.experiments import VisualSSVEP
 
 # Define some variables
-board_name = "muse"
+board_name = "muse2"
 experiment = "visual_ssvep"
 subject_id = 0
 session_nb = 0
@@ -39,4 +39,5 @@
 # Run experiment
 # ---------------------  
 #  
-ssvep.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn)
+ssvep = VisualSSVEP(duration=record_duration, eeg=eeg_device, save_fn=save_fn)
+ssvep.run()