/tmp/bci/machine-learning - Revision history

Ogrisel at 07:33, 13 June 2011

2011-06-13T07:33:58Z

Ogrisel: /* Tools */

2011-01-22T14:57:25Z

‎Tools

Ogrisel: /* Goal */

2011-01-13T17:17:59Z

‎Goal

Ogrisel: /* scikit-learn */

2011-01-13T16:53:07Z

‎scikit-learn

Ogrisel: New page: = Goal = Experiment with basic brain reverse engineering: out of the raw signal of the brain (EEG, fNIRS, ...), try to identify stable correlation with mental activity (reading text, list...

2011-01-13T16:47:38Z

New page: = Goal = Experiment with basic brain reverse engineering: out of the raw signal of the brain (EEG, fNIRS, ...), try to identify stable correlation with mental activity (reading text, list...

New page

= Goal =

Experiment with basic brain reverse engineering: out of the raw signal of the brain (EEG, fNIRS, ...), try to identify stable correlation with mental activity (reading text, listening to music, programming, sleeping) so as to find more deterministic ways to use the kits to control devices or generate artistic outpouts (music, video, ...).

= Tools =

== scikit-learn ==

[http://scikit-learn.sourceforge.net scikit-learn] provides a rich tool box of machine learning algorithms implemented with python / numpy / cython. Approx 50% of the core developers are involved in applications of statistical learning for Neuro-Imaging research.

Some resources to get started:

* https://github.com

= People interested in this subproject =

* Olivier Grisel ([https://twitter.com/ogrisel @ogrisel]) - scikit-learn developer
* Add your name here

← Older revision		Revision as of 07:33, 13 June 2011
Line 4:		Line 4:

	= Tools =		= Tools =
		+
		+	== pythinkgear ==
		+
		+	Python library to gain direct real time access to the NeuroSky MindSet signals from python under Linux and MacOSX (or any system that can map a bluetooth device as a serial port).
		+
		+	Status: currently [https://github.com/ogrisel/pythinkgear being extended] to log the raw data using numpy / matplotlib

	== OpenViBE ==		== OpenViBE ==

← Older revision		Revision as of 14:57, 22 January 2011
Line 4:		Line 4:

	= Tools =		= Tools =
		+
		+	== OpenViBE ==
		+
		+	Graphical environment written in C++ and released under LGPL to build, test and apply EEG and MEG signal decoding / processing in realtime based on user defined scenarios. It provides various signal processing building blocks and a couple of machine learning classifiers (e.g. LDA).
		+
		+	* [http://openvibe.inria.fr/ Project page with video demo, source code, documentation...]

	== scikit-learn ==		== scikit-learn ==

@@ Line 1: / Line 1: @@
 = Goal =
-Experiment with basic brain reverse engineering: out of the raw signal of the brain (EEG, fNIRS, ...), try to identify stable correlation with mental activity (reading text, listening to music, programming, sleeping) so as to find more deterministic ways to use the kits to control devices or generate artistic outpouts (music, video, ...).
+Experiment with basic brain reverse engineering: out of the raw signal of the brain (EEG, fNIRS, ...), try to identify stable correlation with mental activity (reading text, listening to music, programming, sleeping) so as to find more deterministic ways to use the kits to control devices or generate artistic outputs (music, video, ...).
 = Tools =

@@ Line 7: / Line 7: @@
 == scikit-learn ==
-[http://scikit-learn.sourceforge.net scikit-learn] provides a rich tool box of machine learning algorithms implemented with python / numpy / cython. Approx 50% of the core developers are involved in applications of statistical learning for Neuro-Imaging research.
+scikit-learn provides a rich tool box of machine learning algorithms implemented with python / numpy / cython. Approx 50% of the core developers are involved in applications of statistical learning for Neuro-Imaging research.
 Some resources to get started:
-* https://github.com
+* [https://github.com/scikit-learn/scikit-learn Official github source code repo]
 = People interested in this subproject =