An overview of research on" passive" brain-computer interfaces for implicit human-computer interaction
This paper surveys existing and past research on brain-computer interfaces (BCI) for implicit
human-computer interaction. A novel way of using BCI has indeed emerged, which …
human-computer interaction. A novel way of using BCI has indeed emerged, which …
Deep learning human mind for automated visual classification
What if we could effectively read the mind and transfer human visual capabilities to computer
vision methods? In this paper, we aim at addressing this question by developing the first …
vision methods? In this paper, we aim at addressing this question by developing the first …
Hawkes processes for events in social media
This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …
processes, for modeling discrete, inter-dependent events over continuous time. We start by …
Learning from EEG error-related potentials in noninvasive brain-computer interfaces
R Chavarriaga, JR Millán - IEEE transactions on neural …, 2010 - ieeexplore.ieee.org
We describe error-related potentials generated while a human user monitors the
performance of an external agent and discuss their use for a new type of brain-computer …
performance of an external agent and discuss their use for a new type of brain-computer …
Deep learning for video classification and captioning
Deep learning for video classification and captioning Page 1 IPART MULTIMEDIA
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …
A representational similarity analysis of the dynamics of object processing using single-trial EEG classification
The recognition of object categories is effortlessly accomplished in everyday life, yet its
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
Brain2Image Converting Brain Signals into Images
Reading the human mind has been a hot topic in the last decades, and recent research in
neuroscience has found evidence on the possibility of decoding, from neuroimaging data …
neuroscience has found evidence on the possibility of decoding, from neuroimaging data …
Generative adversarial networks conditioned by brain signals
Recent advancements in generative adversarial networks (GANs), using deep convolutional
models, have supported the development of image generation techniques able to reach …
models, have supported the development of image generation techniques able to reach …
Attending to visual stimuli versus performing visual imagery as a control strategy for EEG-based brain-computer interfaces
Currently the most common imagery task used in Brain-Computer Interfaces (BCIs) is motor
imagery, asking a user to imagine moving a part of the body. This study investigates the …
imagery, asking a user to imagine moving a part of the body. This study investigates the …
Developing an efficient functional connectivity-based geometric deep network for automatic EEG-based visual decoding
Neural decoding is of great importance in computational neuroscience to automatically
interpret brain activities in order to address the challenging problem of mind-reading …
interpret brain activities in order to address the challenging problem of mind-reading …