Brain-computer interface research at Katholieke Universiteit Leuven

We present an overview of our Brain-computer interface (BCI) research, invasive as well as non-invasive, during the past four years. The invasive BCIs are based on local field-and action potentials recorded with microelectrode arrays implanted in the visual cortex of the macaque monkey. The non-invasive BCIs are based on electroencephalogram (EEG) recorded from a human subject's scalp. Several EEG paradigms were used to enable the subject to type text or to select icons on a computer screen, without having to rely on one's fingers, gestures, or any other form of motor activity: the P300 event-related potential, the steady-state visual evoked potential, and the error related potential. We report on the status of our EEG BCI tests on healthy subjects as well as patients with severe communication disabilities, and our demonstrations to a broad audience to raise the public awareness of BCI.

[1]  Arne Robben,et al.  Decoding SSVEP Responses using Time Domain Classification , 2018, IJCCI.

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  Nikolay V. Manyakov,et al.  Erratum: “Synchronization in monkey visual cortex analyzed with an information-theoretic measure” [Chaos 18, 037130 (2008)] , 2009 .

[4]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  Marc M. Van Hulle,et al.  Decoding Stimulus-Reward Pairing From Local Field Potentials Recorded From Monkey Visual Cortex , 2010, IEEE Transactions on Neural Networks.

[6]  Nikolay Chumerin,et al.  On the Selection of Time Interval and Frequency Range of EEG Signal Preprocessing for P300 Brain-Computer Interfacing , 2010 .

[7]  G. Orban,et al.  The organization of orientation selectivity throughout macaque visual cortex. , 2002, Cerebral cortex.

[8]  Jonas Poelmans,et al.  Combining ESOMs Trained on a Hierarchy of Feature Subsets for Single-Trial Decoding of LFP Responses in Monkey Area V4 , 2010, ICAISC.

[9]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[10]  Johan A. K. Suykens,et al.  P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface , 2009, KI.

[11]  Antonio Artés-Rodríguez,et al.  Maximization of Mutual Information for Supervised Linear Feature Extraction , 2007, IEEE Transactions on Neural Networks.

[12]  M.M. Van Hulle,et al.  Discriminating visual stimuli from local field potentials recorded with a multi-electrode array in the monkey’s visual cortex , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

[13]  Arne Robben,et al.  Steady State Visual Evoked Potential Based Computer Gaming - The Maze , 2011, INTETAIN.

[15]  Arne Robben,et al.  Subject-adaptive steady-state visual evoked potential detection for brain-computer interface , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.

[16]  Ivan Volosyak,et al.  Impact of Frequency Selection on LCD Screens for SSVEP Based Brain-Computer Interfaces , 2009, IWANN.

[17]  Marc M. Van Hulle,et al.  Comparison of Linear Classification Methods for P300 Brain-computer Interface on Disabled Subjects , 2011, BIOSIGNALS.

[18]  G. Buzsáki.  Rhythms of the brain , 2006 .

[19]  Refet Firat Yazicioglu,et al.  Ultra-low power biopotential interfaces and their application in wearable and implantable systems , 2007 .

[20]  Byron M. Yu,et al.  A high-performance brain–computer interface , 2006, Nature.

[21]  T. J. Sullivan,et al.  A user-friendly SSVEP-based brain–computer interface using a time-domain classifier , 2010, Journal of neural engineering.

[22]  Marc M. Van Hulle,et al.  Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects , 2011, Comput. Intell. Neurosci..

[23]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[24]  Ivan Volosyak,et al.  Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[25]  Nikolay Chumerin,et al.  An application of feature selection to on-line P300 detection in brain-computer interface , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

[26]  Acknowledgments , 2006, Molecular and Cellular Endocrinology.

[27]  Marc M. Van Hulle,et al.  Decoding Grating Orientation from microelectrode Array Recordings in Monkey Cortical Area V4 , 2010, Int. J. Neural Syst..

[28]  Arne Robben,et al.  Combining object detection and brain computer interfacing: Towards a new way of subject-environment interaction , 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

[29]  Katrien Vanderperren,et al.  Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes , 2011 .

[30]  Arne Robben,et al.  Decoding phase-based information from SSVEP recordings: A comparative study , 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

[31]  Arne Robben,et al.  Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface , 2012, Neurocomputing.

[32]  Clay B. Holroyd,et al.  The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. , 2002, Psychological review.

[33]  Arne Robben,et al.  Error-related potential recorded by EEG in the context of a p300 mind speller brain-computer interface , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[35]  Marc M Van Hulle,et al.  Synchronization in monkey visual cortex analyzed with an information-theoretic measure. , 2008, Chaos.

[36]  Arne Robben,et al.  Decoding Phase-Based Information from Steady-State Visual Evoked Potentials with Use of Complex-Valued Neural Network , 2011, IDEAL.

[37]  Johan A. K. Suykens,et al.  Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling , 2009, 2009 International Conference on Machine Learning and Applications.

[38]  Marc M. Van Hulle,et al.  Feature Selection and Feature Extraction Approaches to P300 Detection in On-line Brain-Computer Interface , 2009 .

[39]  Xiaorong Gao,et al.  Frequency and Phase Mixed Coding in SSVEP-Based Brain--Computer Interface , 2011, IEEE Transactions on Biomedical Engineering.

[40]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.