An efficient P300-based brain-computer interface for disabled subjects.

By Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens. Journal of Neuroscience Methods, 2008

945 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0b4668462903d782699bfb5d0b124cbc0f222d84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Jonathan R. Wolpaw
2. Mikhail A. Lebedev
3. Eric W. Sellers
4. Miguel A. L. Nicolelis
5. Andrea Kübler
6. Theresa M. Vaughan
7. Dean J. Krusienski
8. Luis F. Nicolás-Alonso

In Cognitive Neuroscience

1. Jonathan R. Wolpaw
2. Andrea Kübler
3. Mikhail A. Lebedev
4. Eric W. Sellers
5. Miguel A. L. Nicolelis
6. Dean J. Krusienski
7. Theresa M. Vaughan
8. Luis F. Nicolás-Alonso

In Electrical and Electronic Engineering

1. Eric W. Sellers
2. Jonathan R. Wolpaw
3. Miguel A. L. Nicolelis
4. Mikhail A. Lebedev
5. Dean J. Krusienski
6. Luis F. Nicolás-Alonso
7. Jaime Gómez Gil
8. Theresa M. Vaughan

Citations in the corpus

• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “The spatially filtered EEG signals are then classified using Bayesian Linear Discriminant Analysis (BLDA) (see Appendix B of [76] for more details).”
• A comprehensive review of EEG-based brain–computer interface paradigms (2019, neutral): “[2, 109] Single character (SC) speller Scaling data samples into (−1, 1) and”
• xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface (2009, methodology): “no hyperparameters to adjust) [2].”
• A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. (2008, background): “Hoffmann et al. (2008) tested a six-choice visual paradigm that allowed four healthy volunteers and five people severely disabled by ALS to select among icons representing four devices, a door, and a window.”
• The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, neutral): “Neuroscience: calibrating brain-computer interfaces (Lotte and Guan, 2009).”
• Brain Computer Interfaces, a Review (2012, methodology): “BLDA solves the small sample size problem by introducing a statistical method known as regularization.”
• Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “This issue is called the small sample size problem (Hoffmann et al., 2008).”
• Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, neutral): “rosis, and other disorders [115], [116].”
• Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “The P300 component of the response to a visual stimulus, also called P3 (206, 637), has been particularly popular in BMI designs (10, 15, 16, 34, 54, 209, 243, 246, 257, 285, 363, 406, 521, 538, 545, 590, 717, 735, 736, 787, 832) because of its high sensitivity to subjects’ reaction to the stimulus ”