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. A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1766-1773.
. Within-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms. Neuroimage. 2021;237:118127.
. Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users. Arch Phys Med Rehabil [Internet]. 2015;96(3 Suppl):S27-32. http://www.ncbi.nlm.nih.gov/pubmed/25721544
Toward independent home use of brain-computer interfaces a decision algorithm for selection of potential end-users.pdf (457.85 KB)
Toward independent home use of brain-computer interfaces a decision algorithm for selection of potential end-users.pdf (457.85 KB). Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2014;22(4):847-57. http://www.ncbi.nlm.nih.gov/pubmed/24723632 Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.pdf (1.81 MB)
Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.pdf (1.81 MB). A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. J Neural Eng [Internet]. 2014;11(3):035003. http://www.ncbi.nlm.nih.gov/pubmed/24838278 A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.pdf (807.65 KB)
A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.pdf (807.65 KB). Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. Brain Res Bull [Internet]. 2012;87(1):130-4. http://www.ncbi.nlm.nih.gov/pubmed/21985984 Value of amplitude phase and coherence features for a sensorimotor rhythm based brain computer interface.pdf (1.16 MB)
Value of amplitude phase and coherence features for a sensorimotor rhythm based brain computer interface.pdf (1.16 MB). Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography. Conf Proc IEEE Eng Med Biol Soc. 2009. pp. 6465-8.
. Effective brain-computer interfacing using BCI2000. Conf Proc IEEE Eng Med Biol Soc. 2009. pp. 5498-501.
. Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features. Neural Netw [Internet]. 2009;22(9):1257-70. http://www.ncbi.nlm.nih.gov/pubmed/19647981 Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.pdf (5.01 MB)
Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.pdf (5.01 MB). Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. J Neural Eng [Internet]. 2009;6(2):026003. http://www.ncbi.nlm.nih.gov/pubmed/19255462 Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.pdf (469.83 KB)
Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.pdf (469.83 KB). Seizure prediction for epilepsy using a multi-stage phase synchrony based system. Conf Proc IEEE Eng Med Biol Soc [Internet]. 2009;2009:25-8. http://www.ncbi.nlm.nih.gov/pubmed/19965104 james2009.pdf (730.49 KB)
james2009.pdf (730.49 KB). Brain-computer interfaces (BCIs): Detection Instead of Classification. J Neurosci Methods [Internet]. 2008;167(1):51-62. http://www.ncbi.nlm.nih.gov/pubmed/17920134 Brain-computer interfaces (BCIs) - Detection instead of classification.pdf (0 bytes)
Brain-computer interfaces (BCIs) - Detection instead of classification.pdf (0 bytes). Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control. Conf Proc IEEE Eng Med Biol Soc [Internet]. 2008;2008:5224-7. http://www.ncbi.nlm.nih.gov/pubmed/19163895 Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.pdf (1.11 MB)
Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.pdf (1.11 MB). Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification. J Struct Biol [Internet]. 2008;164(1):41-8. http://www.ncbi.nlm.nih.gov/pubmed/18619547 Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.pdf (2.75 MB)
Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.pdf (2.75 MB). Real-time detection of event-related brain activity. Neuroimage [Internet]. 2008;43(2):245-9. http://www.ncbi.nlm.nih.gov/pubmed/18718544 Real-time detection of event-related brain activity.pdf (0 bytes)
Real-time detection of event-related brain activity.pdf (0 bytes). Three cases of feature correlation in an electrocorticographic BCI. Conf Proc IEEE Eng Med Biol Soc. 2008. pp. 5318-21. Three Cases of Feature Correlation in an Electrocorticographic BCI.pdf (0 bytes)
Three Cases of Feature Correlation in an Electrocorticographic BCI.pdf (0 bytes). Three cases of feature correlation in an electrocorticographic BCI. In Engineering in Medicine and Biology Society, 2008. [Internet]. Vancouver, BC: IEEE; 2008. http://www.ncbi.nlm.nih.gov/pubmed/19163918 Three Cases of Feature Correlation in an Electrocorticographic BCI_0.pdf (923.35 KB)
Three Cases of Feature Correlation in an Electrocorticographic BCI_0.pdf (923.35 KB). Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng [Internet]. 2007;4(3):264-75. http://www.ncbi.nlm.nih.gov/pubmed/17873429 Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.pdf (0 bytes)
Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.pdf (0 bytes). An MEG-based brain-computer interface (BCI). Neuroimage [Internet]. 2007;36(3):581-93. http://www.ncbi.nlm.nih.gov/pubmed/17475511 An MEG-based brain-computer interface (BCI).pdf (0 bytes)
An MEG-based brain-computer interface (BCI).pdf (0 bytes). Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG. Conf Proc IEEE Eng Med Biol Soc [Internet]. 2007;2007:3864-7. http://www.ncbi.nlm.nih.gov/pubmed/18002842 Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.pdf (362.78 KB)
Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.pdf (362.78 KB). Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity. Conf Proc IEEE Eng Med Biol Soc [Internet]. 2007;08/2007:5473-6. http://www.ncbi.nlm.nih.gov/pubmed/18003250 james2007.pdf (608.27 KB)
james2007.pdf (608.27 KB). A µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface. IEEE Trans Biomed Eng [Internet]. 2007;54(2):273-80. http://www.ncbi.nlm.nih.gov/pubmed/17278584 A mu-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.pdf (0 bytes)
A mu-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.pdf (0 bytes). The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2006;14(2):153-9. http://www.ncbi.nlm.nih.gov/pubmed/16792282 The BCI Competition III - Validating Alternative Approaches to Actual BCI Problems.pdf (0 bytes)
The BCI Competition III - Validating Alternative Approaches to Actual BCI Problems.pdf (0 bytes). BCI meeting 2005 - Workshop on Technology: Hardware and Software. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2006;14(2):128-31. http://www.ncbi.nlm.nih.gov/pubmed/16792276 BCI Meeting 2005 - Working on Technology - Hardware and Software.pdf (0 bytes)
BCI Meeting 2005 - Working on Technology - Hardware and Software.pdf (0 bytes). Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2006;14(2):183-6. http://www.ncbi.nlm.nih.gov/pubmed/16792289 Classifying EEG and ECoG signals without subject training for fast BCI implementation comparison of nonparalyzed and completely paralyzed subjects.pdf (56.97 KB)
Classifying EEG and ECoG signals without subject training for fast BCI implementation comparison of nonparalyzed and completely paralyzed subjects.pdf (56.97 KB)