The sixth annual MLSP competition, 2010

For the Sixth Annual Machine Learning for Signal Processing competition, sponsored by Nokia and PASCAL2, entrants were asked to develop a classifier, with optional feature extraction, that uses electroencephalography (EEG) data collected during an image presentation (visual odd-ball) task and optimally determines whether each image presented to a user contains or does not contain a pre-specified target. In this paper, we (the organizers of the competition) briefly describe the application, the data, the rules, and the outcomes of the competition. A total of 35 teams entered the contest. Training data were provided. The entries were tested using disjoint test data. The three teams with the best performing entries describe the approach they used in three separate companion papers, all of which appear in this year's conference proceedings.

[1]  Vince D. Calhoun,et al.  The Fourth Annual 2008 MLSP competition , 2008 .

[2]  Touradj Ebrahimi,et al.  Implicit emotional tagging of multimedia using EEG signals and brain computer interface , 2009, WSM@MM.

[3]  T W Picton,et al.  The P300 Wave of the Human Event‐Related Potential , 1992, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[4]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[5]  J. Donoghue,et al.  Designing a Neural Interface System to Restore Mobility , 2009 .

[6]  N. Birbaumer.  Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[7]  Andrzej Cichocki,et al.  Novel Features for Brain-Computer Interfaces , 2007, Comput. Intell. Neurosci..

[8]  Departamento De Biología,et al.  UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA , 2004 .

[9]  Zafer Iscan.  MLSP Competition, 2010: Description of second place method , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[10]  Alain Rakotomamonjy,et al.  MLSP Competition, 2010: Description of third place method. , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[11]  Allan H. Ropper,et al.  Evoked potentials in clinical medicine (second of two parts). , 1982 .

[12]  K. B. Campbell,et al.  Evoked potential correlates of human information processing , 1979, Biological Psychology.

[13]  José Carlos Príncipe,et al.  Brain-Machine Interface Engineering , 2006, Brain-Machine Interface Engineering.

[14]  Jose M. Leiva,et al.  MLSP Competition, 2010: Description of first place method , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[15]  Klaus-Robert Müller,et al.  On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces , 2010, Brain Topography.