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Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade.
A Direct Brain-to-Brain Interface in Humans
Rajesh P. N. Rao, Andrea Stocco, Matthew Bryan, Devapratim Sarma, Tiffany M. Youngquist, Joseph Wu, and Chantel S. Prat · 1932
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, and Jan Peters · 1935
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Analysis of response time distributions in the study of cognitive processes
William E. Hockley · 1939
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Learning non-myopically from human-generated reward
W. Bradley Knox and Peter Stone · 1965
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Temporal Credit Assignment in Reinforcement Learning
Richard S. Sutton · 1984
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Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
Lawrence A. Farwell and Emanuel Donchin · 1988
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart Russell · 1999
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ERP components on reaction errors and their functional significance: a tutorial
Michael Falkenstein, Jorg Hoormann, Stefan Christ, and Joachim Hohnsbein · 2000
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Algorithms for Inverse Reinforcement Learning
Andrew Y. Ng and Stuart Russell · 2000
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The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity
Clay B. Holroyd and Michael G. H. Coles · 2002
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On the need for on-line learning in brain-computer interfaces
Jose del R. Millan · 2004
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Modulation of activity in medial frontal and motor cortices during error observation
Hein T. van Schie, Rogier B. Mars, Michael G. H. Coles, and Harold Bekkering · 2004
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You Are Wrong! - Automatic Detection of Interaction Errors from Brain Waves
Pierre W. Ferrez and Jose del R. Millan · 2005
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Towards adaptive classification for BCI
Pradeep Shenoy, Matthias Krauledat, Benjamin Blankertz, Rajesh P. Rao, and Klaus-Robert Müller · 2006
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Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance
Andrea L. Thomaz and Cynthia Breazeal · 2006
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Error-Related EEG Potentials Generated During Simulated Brain-Computer Interaction
Pierre W. Ferrez and Jose del R. Millan · 2007
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Updating P300: An Integrative Theory of P3a and P3b
John Polich · 2007
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Asymmetric Interpretations of Positive and Negative Human Feedback for a Social Learning Agent
Andrea L. Thomaz and Cynthia Breazeal · 2007
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Teachable robots: Understanding human teaching behavior to build more effective robot learners
Andrea L. Thomaz and Cynthia Breazeal · 2007
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TAMER: Training an Agent Manually via Evaluative Reinforcement
W. Bradley Knox and Peter Stone · 2008
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Everything you wanted to ask about EEG but were afraid to get the right answer
Wlodzimierz Klonowski · 2009
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Interactively shaping agents via human reinforcement: the TAMER framework
W. Bradley Knox and Peter Stone · 2009
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Single-trial analysis and classification of ERP components - A tutorial
Benjamin Blankertz, Steven Lemm, Matthias Treder, Stefan Haufe, and Klaus-Robert Müller · 2010
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Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces
Ricardo Chavarriaga and Jose del R. Millan · 2010
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Robot reinforcement learning using EEG-based reward signals
Inaki Iturrate, Luis Montesano, and Javier Minguez · 2010
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Combining manual feedback with subsequent mdp reward signals for reinforcement learning
W. Bradley Knox and Peter Stone · 2010
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Dynamic Reward Shaping: Training a Robot by Voice
Ana C. Tenorio-Gonzalez, Eduardo F. Morales, and Luis Villasenor-Pineda · 2010
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Predictive information processing in the brain: Errors and response monitoring
Sven Hoffmann and Michael Falkenstein · 2011
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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Learning from human-generated reward
W. Bradley Knox · 2012
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Reinforcement learning from human reward: Discounting in episodic tasks
W. Bradley Knox and Peter Stone · 2012
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Online detection of error-related potentials boosts the performance of mental typewriters
Nico M. Schmidt, Benjamin Blankertz, and Matthias S. Treder · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Error awareness and the error-related negativity: evaluating the first decade of evidence
Jan R. Wessel · 2012
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Policy Shaping: Integrating Human Feedback with Reinforcement Learning
Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell, and Andrea L. Thomaz · 2013
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Task-dependent signal variations in EEG error-related potentials for brain-computer interfaces
Inaki Iturrate, Luis Montesano, and Javier Minguez · 2013
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Classifier Transferability in the Detection of Error Related Potentials from Observation to Interaction
Su Kyoung Kim and Elsa A. Kirchner · 2013
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Training a robot via human feedback: A case study
W. Bradely Knox, Peter Stone, and Cynthia Breazeal · 2013
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Learning via human feedback in continuous state and action spaces
Ngo Anh Vien, Wolfgang Ertel, and Tae Choong Chung · 2013
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Errare machinale est: the use of error-related potentials in brain-machine interfaces
Ricardo Chavarriaga, Aleksander Sobolewski, and Jose del R. Millan · 2014
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What contributes to individual differences in brain structure?
Jenny Gu and Ryota Kanai · 2014
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A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain-Computer Interfaces
Fabien Lotte · 2014
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An Introduction to the Event-Related Potential Technique
Steven J. Luck · 2014
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Handling Few Training Data: Classifier Transfer Between Different Types of Error-Related Potentials
Su Kyoung Kim and Elsa A. Kirchner · 2015
Cited alongside, same era.
Framing reinforcement learning from human reward: Reward positivity, temporal discounting, episodicity, and performance
W. Bradley Knox and Peter Stone · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Cited alongside, same era.
Tackling noise, artifacts and nonstationarity in BCI with robust divergences
Wojciech Samek and Klaus-Robert Müller · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Deep Reinforcement Learning from Policy-Dependent Human Feedback, February 2019
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, and Michael L. Littman · 2019
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An interactive framework for learning continuous actions policies based on corrective feedback
Carlos Celemin and Javier Ruiz-del Solar · 2019
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Deep learning for electroencephalogram (EEG) classification tasks: a review
Alexander Craik, Yongtian He, and Jose L. Contreras-Vidal · 2019
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A Feasibility Study for Validating Robot Actions Using EEG-Based Error-Related Potentials
Stefan K. Ehrlich and Gordon Cheng · 2019
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A Survey on Brain Biometrics
Qiong Gui, Maria V. Ruiz-Blondet, Sarah Laszlo, and Zhanpeng Jin · 2019
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Inferring subjective preferences on robot trajectories using EEG signals
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Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity
Martin Spuler and Christian Niethammer · 2015
Cited alongside, same era.
Concrete Problems in AI Safety, July 2016
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Cited alongside, same era.
OpenAI Gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Robust, accurate spelling based on error-related potentials
Ricardo Chavarriaga, Iñaki Iturrate, and Jose del R. Millan · 2016
Cited alongside, same era.
Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario
Francisco Cruz, German I. Parisi, Johannes Twiefel, and Stefan Wermter · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Feasibility of BCI Control in a Realistic Smart Home Environment
Nataliya Kosmyna, Franck Tarpin-Bernard, Nicolas Bonnefond, and Bertrand Rivet · 2016
Cited alongside, same era.
F. Iwane, M. S. Halvagal, I. Iturrate, I. Batzianoulis, R. Chavarriaga, A. Billard, and J. d R. Millan · 2019
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Embedded Multimodal Interfaces in Robotics: Applications, Future Trends, and Societal Implications , pages 523–576
Elsa A. Kirchner, Stephen H. Fairclough, and Frank Kirchner · 2019
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Human-Centered Reinforcement Learning: A Survey
Guangliang Li, Randy Gomez, Keisuke Nakamura, and Bo He · 2019
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Dota 2 with large scale deep reinforcement learning, 2019
OpenAI, Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander S. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
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Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, and Peter Stone · 2019
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Accelerated robot learning via human brain signals
Iretiayo Akinola, Zizhao Wang, Junyao Shi, Xiaomin He, Pawan Lapborisuth, Jingxi Xu, David Watkins-Valls, Paul Sajda, and Peter Allen · 2020
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jòzefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
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A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges
Christian Arzate Cruz and Takeo Igarashi · 2020
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Ethical issues in advanced artificial intelligence
Nick Bostrom · 2020
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A step closer to becoming symbiotic with AI through EEG: A review of recent BCI technology
S. Dabas, P. Saxena, N. Nordlund, and S. I. Ahamed · 2020
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An empirical investigation of the challenges of real-world reinforcement learning, March 2020
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester · 2020
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Artificial Intelligence, Values, and Alignment
Iason Gabriel · 2020
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A condition-independent framework for the classification of error-related brain activity
Ioannis Kakkos, Errikos M. Ventouras, Pantelis A. Asvestas, Irene S. Karanasiou, and George K. Matsopoulos · 2020
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Flexible online adaptation of learning strategy using EEG-based reinforcement signals in real-world robotic applications
Su Kyoung Kim, Elsa A. Kirchner, and Frank Kirchner · 2020
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A Review on Interactive Reinforcement Learning From Human Social Feedback
Jinying Lin, Zhen Ma, Randy Gomez, Keisuke Nakamura, Bo He, and Guangliang Li · 2020
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Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review
Mamunur Rashid, Norizam Sulaiman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Ahmad Fakhri Ab. Nasir, Bifta Sama Bari, and Sabira Khatun · 2020
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Maximizing BCI Human Feedback using Active Learning
Zizhao Wang, Junyao Shi, Iretiayo Akinola, and Peter Allen · 2020
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Four-Way Classification of EEG Responses To Virtual Robot Navigation
Christopher Wirth, Jake Toth, and Mahnaz Arvaneh · 2020
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You Have Reached Your Destination: A Single Trial EEG Classification Study
Christopher Wirth, Jake Toth, and Mahnaz Arvaneh · 2020
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FRESH: Interactive reward shaping in high-dimensional state spaces using human feedback
Baicen Xiao, Qifan Lu, Bhaskar Ramasubramanian, Andrew Clark, Linda Bushnell, and Radha Poovendran · 2020
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Atari-head: Atari human eye-tracking and demonstration dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl Muller, Jake Whritner, Luxin Zhang, Mary Hayhoe, and Dana Ballard · 2020
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Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials
Iason Batzianoulis, Fumiaki Iwane, Shupeng Wei, Carolina Gaspar Pinto Ramos Correia, Ricardo Chavarriaga, Jose del R. Millan, and Aude Billard · 2021
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Interactive Explanations: Diagnosis and Repair of Reinforcement Learning Based Agent Behaviors
Christian Arzate Cruz and Takeo Igarashi · 2021
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Eeg-based brain-computer interfaces (bcis): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications, 2021
Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, and Chin-Teng Lin · 2021
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Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation
Lin Guan, Mudit Verma, Suna (Sihang) Guo, Ruohan Zhang, and Subbarao Kambhampati · 2021
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What Would Jiminy Cricket Do? Towards Agents That Behave Morally
Dan Hendrycks, Mantas Mazeika, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, and Jacob Steinhardt · 2021
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Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior, April 2021
Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, and Mark Riedl · 2021
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Reinforcement Learning With Human Advice: A Survey
Anis Najar and Mohamed Chetouani · 2021
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Accelerating reinforcement learning using eeg-based implicit human feedback
Duo Xu, Mohit Agarwal, Ekansh Gupta, Faramarz Fekri, and Raghupathy Sivakumar · 2021
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EEG based emotion recognition: A tutorial and review
Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, and Pekka Marttinen · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Error-related potential variability: Exploring the effects on classification and transferability
Benjamin Poole and Minwoo Lee · 2022
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Instilling moral value alignment by means of multi-objective reinforcement learning
Manel Rodriguez-Soto, Marc Serramia, Maite Lopez-Sanchez, and Juan Antonio Rodriguez-Aguilar · 2022
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Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
Inaki Iturrate, Ricardo Chavarriaga, Luis Montesano, Javier Minguez, and Jose del R. Millan · 2045
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BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Linxing Jiang, Andrea Stocco, Darby M. Losey, Justin A. Abernethy, Chantel S. Prat, and Rajesh P. N. Rao · 2045
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Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction
Su Kyoung Kim, Elsa A. Kirchner, Arne Stefes, and Frank Kirchner · 2045
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Online asynchronous decoding of error-related potentials during the continuous control of a robot
Catarina Lopes-Dias, Andreea I. Sburlea, and Gernot R. Müller-Putz · 2045
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