Online detection of error-related potentials in multi-class cognitive task-based BCIs
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[1] Tom Chau,et al. Improving bit rate in an auditory BCI: Exploiting error-related potentials , 2016 .
[2] Tom Chau,et al. Online classification of imagined speech using functional near-infrared spectroscopy signals , 2018, Journal of neural engineering.
[3] Vicenç Gómez,et al. On the use of interaction error potentials for adaptive brain computer interfaces , 2011, Neural Networks.
[4] J. Hohnsbein,et al. Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. , 1991, Electroencephalography and clinical neurophysiology.
[5] M. Stokes,et al. Cognitive tasks for driving a brain-computer interfacing system: a pilot study , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[6] Wolfgang Rosenstiel,et al. Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI , 2012, Clinical Neurophysiology.
[7] Gernot R. Müller-Putz,et al. Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability , 2015, PloS one.
[8] Gernot R Müller-Putz,et al. Masked and unmasked error-related potentials during continuous control and feedback , 2018, Journal of neural engineering.
[9] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.
[10] K. Müller,et al. Predicting BCI performance to study BCI illiteracy , 2009, BMC Neuroscience.
[11] Christian Kothe,et al. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.
[12] Sazali Yaacob,et al. Classification of mental tasks using stockwell transform , 2014, Comput. Electr. Eng..
[13] J. Hohnsbein,et al. ERP components on reaction errors and their functional significance: a tutorial , 2000, Biological Psychology.
[14] Brendan Z. Allison,et al. Comparison of Dry and Gel Based Electrodes for P300 Brain–Computer Interfaces , 2012, Front. Neurosci..
[15] W. A. Sarnacki,et al. Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.
[16] Z. Keirn,et al. A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.
[17] L. Nyberg,et al. Learning by doing versus learning by thinking: An fMRI study of motor and mental training , 2006, Neuropsychologia.
[18] A. Mognon,et al. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.
[19] M. Conson,et al. Selective motor imagery defect in patients with locked-in syndrome , 2008, Neuropsychologia.
[20] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[21] Luca T. Mainardi,et al. Online Detection of P300 and Error Potentials in a BCI Speller , 2010, Comput. Intell. Neurosci..
[22] Rana Fayyaz Ahmad,et al. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques , 2015, Australasian Physical & Engineering Sciences in Medicine.
[23] Abdelkader Benyettou,et al. Hybrid self organizing map and probabilistic quadratic loss multi-class support vector machine for mental tasks classification , 2016 .
[24] Wolfgang Rosenstiel,et al. Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning , 2012, PloS one.
[25] Xingyu Wang,et al. A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials , 2012, Journal of Neuroscience Methods.
[26] 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.
[27] Jason Omedes,et al. Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI , 2018, Journal of neural engineering.
[28] Andrés Úbeda,et al. Mental tasks-based brain-robot interface , 2010, Robotics Auton. Syst..
[29] R. Palaniappan,et al. Utilizing Gamma Band to Improve Mental Task Based Brain-Computer Interface Design , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[30] Martin Spüler,et al. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity , 2015, Front. Hum. Neurosci..
[31] R Chavarriaga,et al. Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[32] G. Pfurtscheller,et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain–computer interface training , 2008, Experimental Brain Research.
[33] Tom Chau,et al. Exploiting error-related potentials in cognitive task based BCI , 2018 .
[34] Christoph Hintermüller,et al. Invariance and variability in interaction error-related potentials and their consequences for classification , 2017, Journal of neural engineering.
[35] G. Pfurtscheller,et al. EEG-based communication: presence of an error potential , 2000, Clinical Neurophysiology.
[36] Jonathan R Wolpaw,et al. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[37] Youxi Wu,et al. Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines , 2011, IEEE Transactions on Magnetics.
[38] José del R. Millán,et al. Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.
[39] S. Cramer,et al. Brain motor system function after chronic, complete spinal cord injury. , 2005, Brain : a journal of neurology.
[40] Jukka Heikkonen,et al. A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.
[41] Andrés Úbeda,et al. SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks , 2015, Neurocomputing.
[42] Erwei Yin,et al. Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[43] Jonathan R Wolpaw,et al. Brain–computer interface systems: progress and prospects , 2007, Expert review of medical devices.
[44] Nicolas Schweighofer,et al. Motor learning without doing: trial-by-trial improvement in motor performance during mental training. , 2010, Journal of neurophysiology.
[45] C. Neuper,et al. The effect of distinct mental strategies on classification performance for brain-computer interfaces. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[46] Ping Wang,et al. Improving Mental Task Classification by Adding High Frequency Band Information , 2008, Journal of Medical Systems.
[47] Amit Konar,et al. Motor imagery and error related potential induced position control of a robotic arm , 2017, IEEE/CAA Journal of Automatica Sinica.
[48] Jaime Gómez Gil,et al. Brain Computer Interfaces, a Review , 2012, Sensors.
[49] R Chavarriaga,et al. Latency correction of event-related potentials between different experimental protocols. , 2014, Journal of neural engineering.
[50] E Donchin,et al. Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[51] Frank Kirchner,et al. Intrinsic interactive reinforcement learning – Using error-related potentials for real world human-robot interaction , 2017, Scientific Reports.
[52] Tom Chau,et al. Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest , 2015, Journal of neural engineering.
[53] Amit Konar,et al. Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose , 2014, Medical & Biological Engineering & Computing.
[54] F. Sepulveda,et al. Localisation of cognitive tasks used in EEG-based BCIs , 2010, Clinical Neurophysiology.
[55] Rajesh Kumar,et al. Classification of mental tasks from EEG data using backtracking search optimization based neural classifier , 2015, Neurocomputing.
[56] Waldemar Karwowski,et al. Detection of error-related negativity in complex visual stimuli: a new neuroergonomic arrow in the practitioner’s quiver , 2017, Ergonomics.
[57] H. Bekkering,et al. Modulation of activity in medial frontal and motor cortices during error observation , 2004, Nature Neuroscience.
[58] Bertrand Olivier,et al. Objective and subjective evaluation of online error correction during P300-based spelling , 2012 .
[59] Farhad Faradji,et al. Toward development of a two-state brain–computer interface based on mental tasks , 2011, Journal of neural engineering.
[60] I Iturrate,et al. Task-dependent signal variations in EEG error-related potentials for brain–computer interfaces , 2013, Journal of neural engineering.
[61] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[62] Steven C. Cramer,et al. Effects of motor imagery training after chronic, complete spinal cord injury , 2007, Experimental Brain Research.
[63] C. Neuper,et al. Whatever Works: A Systematic User-Centered Training Protocol to Optimize Brain-Computer Interfacing Individually , 2013, PloS one.
[64] Ricardo Chavarriaga,et al. Errare machinale est: the use of error-related potentials in brain-machine interfaces , 2014, Front. Neurosci..
[65] L M Collins,et al. Applying dynamic data collection to improve dry electrode system performance for a P300-based brain–computer interface , 2016, Journal of neural engineering.
[66] Gernot R. Müller-Putz,et al. Control of an Electrical Prosthesis With an SSVEP-Based BCI , 2008, IEEE Transactions on Biomedical Engineering.
[67] Vera Kaiser,et al. Cortical effects of user training in a motor imagery based brain–computer interface measured by fNIRS and EEG , 2014, NeuroImage.
[68] Christa Neuper,et al. Error potential detection during continuous movement of an artificial arm controlled by brain–computer interface , 2012, Medical & Biological Engineering & Computing.