Decision-making in brains and robots—The case for an interdisciplinary approach
Reinforcement Learning describes a general method for trial-and-error learning, and it has
emerged as a dominant framework both for optimal control in autonomous robots, and …
emerged as a dominant framework both for optimal control in autonomous robots, and …
Neural computations for brain machine interface applications
YH Kang, A Khorasani, RD Flint, B Farrokhi… - Frontiers in Human …, 2023 - frontiersin.org
Along with a flowering of deep learning, there has been a renaissance of brain-computer
interface (BCI) research. One of the most active research areas of the BCI aims attention at a …
interface (BCI) research. One of the most active research areas of the BCI aims attention at a …
Decoding learning strategies from EEG signals provides generalizable features for decoding decision
Recent studies have demonstrated that learning strategies can be decoded from EEG data
using a computational model of model-based and model-free reinforcement learning. The …
using a computational model of model-based and model-free reinforcement learning. The …
Meta-BCI: Perspectives on a role of self-supervised learning in meta brain computer interface
A time-efficient process for building a decoder transforming neural signals into intended
commands must be considered equally significantly as maximizing its interpretation …
commands must be considered equally significantly as maximizing its interpretation …
Decoding both intention and learning strategies from EEG signals
Despite the fact that a majority of Brain-Computer Interface (BCI) studies have focused on
decoding signals related to movement, decoding intention underlying movement might be …
decoding signals related to movement, decoding intention underlying movement might be …
Decoding prefrontal cognitive states from electroencephalography in virtual-reality environment
D Kim, J Park, J Hwang, WH Cho… - 2020 8th International …, 2020 - ieeexplore.ieee.org
Rapid advances in deep learning enabled us to develop various brain-computer interface
(BCI) applications. This study presents a novel BCI framework in virtual-reality environment …
(BCI) applications. This study presents a novel BCI framework in virtual-reality environment …
Goal-Driven Atari Environment
Recent studies have found that human strategic decision-making is well explained by a
mixture of model-based (MB) and model-free reinforcement learning (MF)[1], and the …
mixture of model-based (MB) and model-free reinforcement learning (MF)[1], and the …