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authorTimotej Lazar <timotej.lazar@fri.uni-lj.si>2017-06-20 18:54:59 +0200
committerTimotej Lazar <timotej.lazar@fri.uni-lj.si>2017-06-20 18:54:59 +0200
commit99bbb6cd39e9491fef3bdcbcf40235c21a479c5d (patch)
tree58b43d1e22d8daf8b38c5f6b4b354ea89e2e7637 /2017
parentbea5b9ff3689787a4107ee6634be47413f3fc4d5 (diff)
parent5740ed3123e97d5c424340d613515d965087aefc (diff)
Add '2017/free-will/' from commit '5740ed3123e97d5c424340d613515d965087aefc'
git-subtree-dir: 2017/free-will git-subtree-mainline: bea5b9ff3689787a4107ee6634be47413f3fc4d5 git-subtree-split: 5740ed3123e97d5c424340d613515d965087aefc
Diffstat (limited to '2017')
-rw-r--r--2017/free-will/LICENSE674
-rw-r--r--2017/free-will/README.md14
-rw-r--r--2017/free-will/openvibe-scenarios/free-will-bci-1-acquisition.xml1204
-rw-r--r--2017/free-will/openvibe-scenarios/free-will-bci-2-classifier-trainer.xml695
-rw-r--r--2017/free-will/scripts/python-learning.py157
-rw-r--r--2017/free-will/signals/record-sig-[2017.05.08-11.08.24].csv3
-rw-r--r--2017/free-will/signals/record-sig-[2017.05.15-23.11.00].csv3
-rw-r--r--2017/free-will/signals/record-stim-[2017.05.08-11.08.24].csv3
-rw-r--r--2017/free-will/signals/record-stim-[2017.05.15-23.11.00].csv3
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diff --git a/2017/free-will/LICENSE b/2017/free-will/LICENSE
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+++ b/2017/free-will/LICENSE
@@ -0,0 +1,674 @@
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+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation. If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+ If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+ Later license versions may give you additional or different
+permissions. However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+ 15. Disclaimer of Warranty.
+
+ THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ {one line to give the program's name and a brief idea of what it does.}
+ Copyright (C) {year} {name of author}
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ {project} Copyright (C) {year} {fullname}
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/2017/free-will/README.md b/2017/free-will/README.md
new file mode 100644
index 0000000..378a2fd
--- /dev/null
+++ b/2017/free-will/README.md
@@ -0,0 +1,14 @@
+# free-will
+AI project - Disprove the existence of the free will
+
+My attempt for the "Free will experiment" using Emotiv EPOC EEG headset and OpenViBE platform (the idea for the experiment can be seen in this youtube video - https://www.youtube.com/watch?v=lmI7NnMqwLQ ). This project could have been done better with additional filters to get rid of noise/blinks etc. and more precise EEG data.
+
+For receiving data from the EPOC headset, I used hemokit (https://github.com/nh2/hemokit) with following command:
+hemokit-dump.exe --format sensorbytes --serve 127.0.0.1:1337 --realtime
+
+OpenViBE collects this data with acquisition server as described in https://github.com/nh2/hemokit/wiki/OpenVibe .
+
+OpenViBE scenarios that I used are located in the openvibe-scenarios folder:
+* free-will-bci-1-acquisition.xml - saving EEG data to CSV file, saving keyboard button presses into CSV file
+* free-will-bci-2-classifier-trainer.xml - scenario with just a python script (see scripts/python-learning.py)
+
diff --git a/2017/free-will/openvibe-scenarios/free-will-bci-1-acquisition.xml b/2017/free-will/openvibe-scenarios/free-will-bci-1-acquisition.xml
new file mode 100644
index 0000000..523ade9
--- /dev/null
+++ b/2017/free-will/openvibe-scenarios/free-will-bci-1-acquisition.xml
@@ -0,0 +1,1204 @@
+<OpenViBE-Scenario>
+ <Creator>OpenViBE</Creator>
+ <CreatorVersion>1.3.0</CreatorVersion>
+ <Boxes>
+ <Box>
+ <Identifier>(0x000011b9, 0x00002dbe)</Identifier>
+ <Name>Stimulation multiplexer</Name>
+ <AlgorithmClassIdentifier>(0x07db4efa, 0x472b0938)</AlgorithmClassIdentifier>
+ <Inputs>
+ <Input>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Input stimulations 1</Name>
+ </Input>
+ <Input>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Input stimulations 2</Name>
+ </Input>
+ </Inputs>
+ <Outputs>
+ <Output>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Multiplexed stimulations</Name>
+ </Output>
+ </Outputs>
+ <Attributes>
+ <Attribute>
+ <Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
+ <Value>-48.000000</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
+ <Value>34</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x207c9054, 0x3c841b63)</Identifier>
+ <Value>640.000000</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x4e7b798a, 0x183beafb)</Identifier>
+ <Value>(0xd5da9c1d, 0x1cc7dbf9)</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xad100179, 0xa3c984ab)</Identifier>
+ <Value>117</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
+ <Value>(0x00000000, 0x005f6b9c)</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xc73e83ec, 0xf855c5bc)</Identifier>
+ <Value>false</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xc80ce8af, 0xf699f813)</Identifier>
+ <Value>1</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xcfad85b0, 0x7c6d841c)</Identifier>
+ <Value>2</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xfba64161, 0x65304e21)</Identifier>
+ <Value></Value>
+ </Attribute>
+ </Attributes>
+ </Box>
+ <Box>
+ <Identifier>(0x00001ab6, 0x00007dc8)</Identifier>
+ <Name>Stimulation listener</Name>
+ <AlgorithmClassIdentifier>(0x65731e1d, 0x47de5276)</AlgorithmClassIdentifier>
+ <Inputs>
+ <Input>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Stimulation stream 1</Name>
+ </Input>
+ </Inputs>
+ <Settings>
+ <Setting>
+ <TypeIdentifier>(0xa88b3667, 0x0871638c)</TypeIdentifier>
+ <Name>Log level to use</Name>
+ <DefaultValue>Debug</DefaultValue>
+ <Value>Information</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ </Settings>
+ <Attributes>
+ <Attribute>
+ <Identifier>(0x1fa7a38f, 0x54edbe0b)</Identifier>
+ <Value>16.000000</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x1fa963f5, 0x1a638cd4)</Identifier>
+ <Value>34</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x207c9054, 0x3c841b63)</Identifier>
+ <Value>656.000000</Value>
+ </Attribute>
+ <Attribute>
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+ <Value>(0xf451ad91, 0x14c75f86)</Value>
+ </Attribute>
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+ </Attribute>
+ <Attribute>
+ <Identifier>(0xc46b3d00, 0x3e0454e1)</Identifier>
+ <Value>(0x00000000, 0x00274198)</Value>
+ </Attribute>
+ <Attribute>
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+ <Value>false</Value>
+ </Attribute>
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+ <Value>1</Value>
+ </Attribute>
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+ <Value>1</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xfba64161, 0x65304e21)</Identifier>
+ <Value></Value>
+ </Attribute>
+ </Attributes>
+ </Box>
+ <Box>
+ <Identifier>(0x00002750, 0x00001fb2)</Identifier>
+ <Name>Signal display</Name>
+ <AlgorithmClassIdentifier>(0x0055be5f, 0x087bdd12)</AlgorithmClassIdentifier>
+ <Inputs>
+ <Input>
+ <TypeIdentifier>(0x5ba36127, 0x195feae1)</TypeIdentifier>
+ <Name>Data</Name>
+ </Input>
+ <Input>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Stimulations</Name>
+ </Input>
+ <Input>
+ <TypeIdentifier>(0x6ab26b81, 0x0f8c02f3)</TypeIdentifier>
+ <Name>Channel Units</Name>
+ </Input>
+ </Inputs>
+ <Settings>
+ <Setting>
+ <TypeIdentifier>(0x5de046a6, 0x086340aa)</TypeIdentifier>
+ <Name>Display Mode</Name>
+ <DefaultValue>Scan</DefaultValue>
+ <Value>Scan</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x33a30739, 0x00d5299b)</TypeIdentifier>
+ <Name>Auto vertical scale</Name>
+ <DefaultValue>Per channel</DefaultValue>
+ <Value>Per channel</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
+ <Name>Scale refresh interval (secs)</Name>
+ <DefaultValue>5</DefaultValue>
+ <Value>5</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
+ <Name>Vertical Scale</Name>
+ <DefaultValue>100</DefaultValue>
+ <Value>100</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
+ <Name>Vertical Offset</Name>
+ <DefaultValue>0</DefaultValue>
+ <Value>0</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x512a166f, 0x5c3ef83f)</TypeIdentifier>
+ <Name>Time Scale</Name>
+ <DefaultValue>10</DefaultValue>
+ <Value>10</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
+ <Name>Bottom ruler</Name>
+ <DefaultValue>true</DefaultValue>
+ <Value>true</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
+ <Name>Left ruler</Name>
+ <DefaultValue>false</DefaultValue>
+ <Value>false</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x2cdb2f0b, 0x12f231ea)</TypeIdentifier>
+ <Name>Multiview</Name>
+ <DefaultValue>false</DefaultValue>
+ <Value>false</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ </Settings>
+ <Attributes>
+ <Attribute>
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+ </Attribute>
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+ <Attribute>
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+ <Value></Value>
+ </Attribute>
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+ </Attribute>
+ <Attribute>
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+ </Attribute>
+ <Attribute>
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+ <Value>3</Value>
+ </Attribute>
+ </Attributes>
+ </Box>
+ <Box>
+ <Identifier>(0x000032a2, 0x00004de0)</Identifier>
+ <Name>Keyboard stimulator</Name>
+ <AlgorithmClassIdentifier>(0x00d317b9, 0x6324c3ff)</AlgorithmClassIdentifier>
+ <Outputs>
+ <Output>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>Outgoing Stimulations</Name>
+ </Output>
+ </Outputs>
+ <Settings>
+ <Setting>
+ <TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
+ <Name>Filename</Name>
+ <DefaultValue>${Path_Data}/plugins/stimulation/simple-keyboard-to-stimulations.txt</DefaultValue>
+ <Value>${Path_Data}/plugins/stimulation/simple-keyboard-to-stimulations.txt</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
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+ <Box>
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+ <TypeIdentifier>(0xcc14d8d6, 0xf27ecb73)</TypeIdentifier>
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+ <Name>Filename</Name>
+ <DefaultValue>record-[$core{date}-$core{time}].csv</DefaultValue>
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+Keyboard stimulations are also logged (and saved to csv file):
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+ &lt;i&gt;z&lt;/i&gt; finishes this scenario.</Text>
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+ <Attribute>
+ <Identifier>(0xf6b2e3fa, 0x7bd43926)</Identifier>
+ <Value></Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xf8034a49, 0x8b3f37cc)</Identifier>
+ <Value></Value>
+ </Attribute>
+ </Attributes>
+</OpenViBE-Scenario> \ No newline at end of file
diff --git a/2017/free-will/openvibe-scenarios/free-will-bci-2-classifier-trainer.xml b/2017/free-will/openvibe-scenarios/free-will-bci-2-classifier-trainer.xml
new file mode 100644
index 0000000..b665bb8
--- /dev/null
+++ b/2017/free-will/openvibe-scenarios/free-will-bci-2-classifier-trainer.xml
@@ -0,0 +1,695 @@
+<OpenViBE-Scenario>
+ <Creator>OpenViBE</Creator>
+ <CreatorVersion>1.3.0</CreatorVersion>
+ <Boxes>
+ <Box>
+ <Identifier>(0x00000124, 0x0000214f)</Identifier>
+ <Name>Python scripting</Name>
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+ <Output>
+ <TypeIdentifier>(0x6f752dd0, 0x082a321e)</TypeIdentifier>
+ <Name>EndStimulation</Name>
+ </Output>
+ </Outputs>
+ <Settings>
+ <Setting>
+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>Clock frequency (Hz)</Name>
+ <DefaultValue>64</DefaultValue>
+ <Value>64</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0xb0d0db45, 0x49cbc34a)</TypeIdentifier>
+ <Name>Script</Name>
+ <DefaultValue></DefaultValue>
+ <Value>E:/Tilen/FAX/2. stopnja/UI/projekt-bci/free-will/scripts/python-learning.py</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
+ <Name>InputCSVSignal</Name>
+ <DefaultValue></DefaultValue>
+ <Value>E:/Tilen/FAX/2. stopnja/UI/projekt-bci/free-will/signals/record-sig-[2017.05.15-23.11.00].csv</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
+ <Name>InputCSVStimulations</Name>
+ <DefaultValue></DefaultValue>
+ <Value>E:/Tilen/FAX/2. stopnja/UI/projekt-bci/free-will/signals/record-stim-[2017.05.15-23.11.00].csv</Value>
+ <Modifiability>false</Modifiability>
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+ <Setting>
+ <TypeIdentifier>(0x330306dd, 0x74a95f98)</TypeIdentifier>
+ <Name>SaveFile</Name>
+ <DefaultValue></DefaultValue>
+ <Value></Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>WindowSize (ms)</Name>
+ <DefaultValue>100</DefaultValue>
+ <Value>100</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
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+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>NumOfPrevWindows</Name>
+ <DefaultValue>5</DefaultValue>
+ <Value>4</Value>
+ <Modifiability>false</Modifiability>
+ </Setting>
+ <Setting>
+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>NumOfWindowsBefore</Name>
+ <DefaultValue>0</DefaultValue>
+ <Value>4</Value>
+ <Modifiability>false</Modifiability>
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+ <Setting>
+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>NumOfWindowsAfter</Name>
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+ <Modifiability>false</Modifiability>
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+ <Setting>
+ <TypeIdentifier>(0x007deef9, 0x2f3e95c6)</TypeIdentifier>
+ <Name>K-folds</Name>
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+ <Value>10</Value>
+ <Modifiability>false</Modifiability>
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+ </Attributes>
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+ <Box>
+ <Identifier>(0x000055e6, 0x00000489)</Identifier>
+ <Name>Player Controller</Name>
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+ <Settings>
+ <Setting>
+ <TypeIdentifier>(0x2c132d6e, 0x44ab0d97)</TypeIdentifier>
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+ <DefaultValue>OVTK_StimulationId_Label_00</DefaultValue>
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+ <Setting>
+ <TypeIdentifier>(0xcc14d8d6, 0xf27ecb73)</TypeIdentifier>
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+ </Links>
+ <MessageLinks></MessageLinks>
+ <Comments>
+ <Comment>
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+ <Text>&lt;u&gt;&lt;b&gt;&lt;big&gt;Overview&lt;/big&gt;&lt;/b&gt;&lt;/u&gt;
+
+This scenario is used to train data for the &quot;free will experiment&quot;.
+See the python scripts for a deeper look into the code.
+
+Main Python script settings are:
+ &lt;b&gt;Script&lt;/b&gt; - python script for learning and other ML methods
+ &lt;b&gt;InputCSVSignal&lt;/b&gt; - captured EEG signal
+ &lt;b&gt;InputCSVStimulation&lt;/b&gt; - captured keyboard times
+ &lt;b&gt;SaveFile&lt;/b&gt; - if you want to save learning hypotesis into a (python pickle) file (e.g. ./python-out-[$core{date}-$core{time}].pkl)
+ &lt;b&gt;WindowSize (ms)&lt;/b&gt; - size of windows/chunks in milliseconds
+ &lt;b&gt;NumberOfPrevWindows&lt;/b&gt; - number of windows, to be marked as class 1 (just before the button was pressed)
+ &lt;b&gt;NumberOfWindowsBefore&lt;/b&gt; - number of windows, to be marked as class 0 (before those marked as 1)
+ &lt;b&gt;NumberOfWindowsAfter&lt;/b&gt; - number of windows, to be marked as class 0 (after those marked as 1)
+ &lt;b&gt;K-folds&lt;/b&gt; - K-fold cross validation</Text>
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+ </Attributes>
+ </Comment>
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+ <VisualisationWidget>
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+ </VisualisationWidget>
+ <VisualisationWidget>
+ <Identifier>(0x0c60317a, 0x7480157f)</Identifier>
+ <Name>Default window</Name>
+ <Type>1</Type>
+ <ParentIdentifier>(0xffffffff, 0xffffffff)</ParentIdentifier>
+ <BoxIdentifier>(0xffffffff, 0xffffffff)</BoxIdentifier>
+ <NumChildren>1</NumChildren>
+ <Attributes>
+ <Attribute>
+ <Identifier>(0x4c90d4ad, 0x7a2554ec)</Identifier>
+ <Value>1</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x7b814cca, 0x271df6dd)</Identifier>
+ <Value>1</Value>
+ </Attribute>
+ </Attributes>
+ </VisualisationWidget>
+ <VisualisationWidget>
+ <Identifier>(0x4eff2d5e, 0x057e5a93)</Identifier>
+ <Name>Default tab</Name>
+ <Type>2</Type>
+ <ParentIdentifier>(0x0c60317a, 0x7480157f)</ParentIdentifier>
+ <Index>0</Index>
+ <BoxIdentifier>(0xffffffff, 0xffffffff)</BoxIdentifier>
+ <NumChildren>1</NumChildren>
+ </VisualisationWidget>
+ <VisualisationWidget>
+ <Identifier>(0x00b5ee2a, 0x40e0a42c)</Identifier>
+ <Name>Empty</Name>
+ <Type>0</Type>
+ <ParentIdentifier>(0x4eff2d5e, 0x057e5a93)</ParentIdentifier>
+ <Index>0</Index>
+ <BoxIdentifier>(0xffffffff, 0xffffffff)</BoxIdentifier>
+ <NumChildren>0</NumChildren>
+ </VisualisationWidget>
+ </VisualisationTree>
+ <Attributes>
+ <Attribute>
+ <Identifier>(0x790d75b8, 0x3bb90c33)</Identifier>
+ <Value>Yann Renard / Fabien Lotte</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x8c1fc55b, 0x7b433dc2)</Identifier>
+ <Value></Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0x9f5c4075, 0x4a0d3666)</Identifier>
+ <Value>Classifier Training</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xf36a1567, 0xd13c53da)</Identifier>
+ <Value></Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xf6b2e3fa, 0x7bd43926)</Identifier>
+ <Value>Motor Imagery</Value>
+ </Attribute>
+ <Attribute>
+ <Identifier>(0xf8034a49, 0x8b3f37cc)</Identifier>
+ <Value>INRIA</Value>
+ </Attribute>
+ </Attributes>
+</OpenViBE-Scenario> \ No newline at end of file
diff --git a/2017/free-will/scripts/python-learning.py b/2017/free-will/scripts/python-learning.py
new file mode 100644
index 0000000..d144678
--- /dev/null
+++ b/2017/free-will/scripts/python-learning.py
@@ -0,0 +1,157 @@
+import numpy as np
+import pandas
+from math import ceil, floor
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+from sklearn.model_selection import KFold, StratifiedKFold, permutation_test_score
+from sklearn import linear_model
+from sklearn.svm import SVC
+import pickle
+import matplotlib.pyplot as plt
+import numpy.fft as fft
+from sklearn import datasets
+
+class MyOVBox(OVBox):
+ def __init__(self):
+ OVBox.__init__(self)
+ # Names of CSV files
+ self.signalFileName = ""
+ self.stimFileName = ""
+ # Name of Save File
+ self.saveFileName = ""
+ # other variables
+ self.windowSize = 0 # window size in ms
+ self.numOfPreviousWindowsAsOne = 0 # number of windows before actual stimulation to be marked as 1
+ self.numOfWindowsBefore = 0 # number of windows before those marked as 1, to be marked as 0
+ self.numOfWindowsAfter = 0 # number of windows after those marked as 1, to be marked as 0
+
+
+ def filter_signal(self, sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal):
+ """ returns tuple of filtered (signal chunks, classes) """
+ splittedSignal_filtrd = []
+ classes_filtrd = []
+
+ temp_classes = np.zeros(len(splittedSignal))
+
+ for stim in stimulationTimes:
+ index = int(floor(stim*sampleRate/numberOfSamplesWindow))
+ temp_classes[index] = 1
+ for i in range(1, self.numOfPreviousWindowsAsOne):
+ temp_classes[index-i] = 1
+
+ tmp_cls_winds = temp_classes[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter]
+ tmp_sig_winds = np.concatenate(splittedSignal[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter])
+
+ if len(tmp_sig_winds)/len(tmp_cls_winds)!=numberOfSamplesWindow: # if np.array_split does not split in equal windows
+ tmp_sig_winds = np.lib.pad(tmp_sig_winds, ((0, int(len(tmp_cls_winds)*numberOfSamplesWindow-len(tmp_sig_winds))),(0, 0)), 'edge') # pad with same values on end of array
+
+ classes_filtrd.extend(tmp_cls_winds)
+ splittedSignal_filtrd.extend(np.array_split(tmp_sig_winds, len(tmp_cls_winds)))
+
+ return (splittedSignal_filtrd, classes_filtrd)
+
+
+ def avg_k_fold(self, data, classes, k=4):
+ """ return average CA of k-fold cross validation """
+ avg_val = 0
+
+ kf = KFold(n_splits=k)
+ for train, test in kf.split(data):
+ clf = svm.SVC(kernel='linear', C=1).fit(data[train], classes[train])
+ cur_score = clf.score(data[test], classes[test])
+ avg_val += cur_score
+ # print cur_score
+ return avg_val/k
+
+
+ def permutation_significance_classification_score(self, X, y, k_folds=4):
+ n_classes = np.unique(y).size
+
+ svm = SVC(kernel='linear')
+ cv = StratifiedKFold(k_folds)
+
+ score, permutation_scores, pvalue = permutation_test_score(svm, X, y, scoring="accuracy", cv=cv, n_permutations=200, n_jobs=1)
+ print("Classification score %s (pvalue : %s)" % (score, pvalue))
+
+ plt.hist(permutation_scores, 20, label='Permutation scores')
+ ylim = plt.ylim()
+ plt.plot(2 * [score], ylim, '--g', linewidth=3,
+ label='Classification Score'
+ ' (pvalue %s)' % pvalue)
+ plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
+
+ plt.ylim(ylim)
+ plt.legend()
+ plt.xlabel('Score')
+ plt.show()
+
+
+ def initialize(self):
+ # Names of CSV files
+ self.signalFileName = self.setting['InputCSVSignal']
+ self.stimFileName = self.setting['InputCSVStimulations']
+ # Name of Save File
+ self.saveFileName = self.setting['SaveFile']
+ # other variables
+ self.windowSize = int(self.setting['WindowSize (ms)']) # in ms
+ self.numOfPreviousWindowsAsOne = int(self.setting['NumOfPrevWindows'])
+ self.numOfWindowsBefore = int(self.setting['NumOfWindowsBefore'])-1
+ self.numOfWindowsAfter = int(self.setting['NumOfWindowsAfter'])+1
+ self.k_folds = int(self.setting['K-folds'])
+
+ print "Reading files..."
+ # read CSV files
+ signalArray = pandas.read_csv(self.signalFileName, delimiter=";", encoding="utf-8-sig")
+ stimsArray = pandas.read_csv(self.stimFileName, delimiter=";", encoding="utf-8-sig")
+ print "Files read!"
+
+ # sort information from tables (pandas dataframe)
+ time = signalArray['Time (s)']
+ electrodes = signalArray.iloc[:, 1:signalArray.shape[1]-1]
+ sampleRate = signalArray['Sampling Rate'][0]
+ stimulationTimes = stimsArray['Time (s)']
+
+ numberOfSamplesWindow = floor(self.windowSize / ((1/sampleRate)*1000)) # number of samples for approximately self.windowSize ms
+
+ splittedSignal = np.array_split(electrodes, ceil(len(time)/numberOfSamplesWindow)) # split signal into chunks of specified length
+
+ s_c = self.filter_signal(sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal) # filter signal and return sparsed version of chunks and assign classes
+ splittedSignal_filtrd = s_c[0]
+ classes_filtrd = s_c[1]
+
+ splittedSignal_filtrd_means = np.array(np.mean(splittedSignal_filtrd, axis=1)) # for each window calculate mean value
+ # additional attributes could be added besides splittedSignal_filtrd_means
+ classes_filtrd = np.array(classes_filtrd)
+
+ # print average k-fold CA
+ print "Average " + str(self.k_folds) + "-folds value: " + str(self.avg_k_fold(splittedSignal_filtrd_means, classes_filtrd, k=self.k_folds))
+ #self.permutation_significance_classification_score(splittedSignal_filtrd_means, classes_filtrd, k_folds=2) # k_folds=2, last long if k is bigger
+
+ if self.saveFileName:
+ clf = svm.SVC(kernel='linear', C=1).fit(splittedSignal_filtrd_means, classes_filtrd)
+ clf.fit(splittedSignal_filtrd_means, classes_filtrd)
+
+ print "Saving to pickle file: " + self.saveFileName
+ pickle.dump(clf, open(self.saveFileName, 'wb'))
+ print "Learned score: " + str(clf.score(splittedSignal_filtrd_means, classes_filtrd))
+
+ # send finish stimulation output (for OpenViBE)
+ self.finishBySendingStimulation(32774) # OVTK_StimulationId_TrialStop code
+
+
+ def finishBySendingStimulation(self, stimulationCode):
+ stimSetFinish = OVStimulationSet(self.getCurrentTime(), self.getCurrentTime()+1./self.getClock())
+ stimSetFinish.append(OVStimulation(stimulationCode, self.getCurrentTime(), 0.))
+ self.output[0].append(stimSetFinish)
+
+
+ def process(self):
+
+ return
+
+ def uninitialize(self):
+ # nop
+
+ return
+
+box = MyOVBox()
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