On the Need for On-Line Learning in Brain-Computer Interfaces
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This paper describes the design of a robot agent and associated learning algorithms to help children in handwriting acquisition. The main issue lies in how to program a robot to obtain human-like handwriting and then exploit it to teach children. We propos ...
Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the ...
Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks ...
In the last years there has been an increasing interest on using human feedback during robot operation to incorporate non-expert human expertise while learning complex tasks. Most work has considered reinforcement learning frameworks were human feedback, p ...
Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) ...
Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the ...
In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges ...
We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the ...
Neurofeedback therapies are an emerging technique used to treat neuropsychological disorders and to enhance cognitive performance. The feedback stimuli presented during the therapy are a key factor, serving as guidance throughout the entire learning proces ...
Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, ...