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Machine Learning and Signal Processing Tools for BCI

This video was recorded at BBCI Workshop: Advances in Neurotechnologies, Berlin 2009. We will first provide a brief overview of Brain-Computer Interface from a machine learning and signal processing perspective. In particular showing the wealth, the complexity and the difficulties of the data available, a truly enormous challenge: In real-time a multi-variate very strongly noise contaminated data stream is to be processed and neuroelectric activities are to be accurately differentiated. We will then in detail discuss the components of the data analysis chain employed in modern BCI systems, spanning all aspects from preprocessing and feature extraction, adaptive vs. fixed classification and feedback design.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Authors:: Klaus-Robert Müller, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin
Benjamin Blankertz, Machine Learning and Intelligent Data Analysis Group, TU Berlin
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States