Impact of Frequency Selection on LCD Screens for SSVEP Based Brain-Computer Interfaces
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems
Microelectronics Journal
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
P300 detection based on feature extraction in on-line brain-computer interface
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Decoding stimulus-reward pairing from local field potentials recorded from monkey visual cortex
IEEE Transactions on Neural Networks
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Maximization of Mutual Information for Supervised Linear Feature Extraction
IEEE Transactions on Neural Networks
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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.