Multimodal classification: Current landscape, taxonomy and future directions
Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
The Berlin brain-computer interface: progress beyond communication and control
The combined effect of fundamental results about neurocognitive processes and
advancements in decoding mental states from ongoing brain signals has brought forth a …
advancements in decoding mental states from ongoing brain signals has brought forth a …
EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy
Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems
are mainly divided into three major paradigms: motor imagery (MI), event-related potential …
are mainly divided into three major paradigms: motor imagery (MI), event-related potential …
A convolutional neural network for steady state visual evoked potential classification under ambulatory environment
The robust analysis of neural signals is a challenging problem. Here, we contribute a
convolutional neural network (CNN) for the robust classification of a steady-state visual …
convolutional neural network (CNN) for the robust classification of a steady-state visual …
[HTML][HTML] Decoding the auditory brain with canonical component analysis
The relation between a stimulus and the evoked brain response can shed light on
perceptual processes within the brain. Signals derived from this relation can also be …
perceptual processes within the brain. Signals derived from this relation can also be …
Open access dataset for EEG+ NIRS single-trial classification
We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using
electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we …
electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we …
Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset
We provide an open access multimodal brain-imaging dataset of simultaneous
electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty …
electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty …
A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
A high-security EEG-based login system with RSVP stimuli and dry electrodes
Lately, electroencephalography (EEG)-based auth-entication has received considerable
attention from the scientific community. However, the limited usability of wet EEG electrodes …
attention from the scientific community. However, the limited usability of wet EEG electrodes …
M3BA: a mobile, modular, multimodal biosignal acquisition architecture for miniaturized EEG-NIRS-based hybrid BCI and monitoring
A von Lühmann, H Wabnitz, T Sander… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Objective: For the further development of the fields of telemedicine, neurotechnology, and
brain-computer interfaces, advances in hybrid multimodal signal acquisition and processing …
brain-computer interfaces, advances in hybrid multimodal signal acquisition and processing …