Brain wave classification using long short-term memory network based OPTICAL predictor
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater
accuracy are highly desirable. To this end, a number of techniques have been proposed …
accuracy are highly desirable. To this end, a number of techniques have been proposed …
An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information
Background Common spatial pattern (CSP) has been an effective technique for feature
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
Estimating systemic cognitive states from a mixture of physiological and brain signals
As human–machine teams are being considered for a variety of mixed‐initiative tasks,
detecting and being responsive to human cognitive states, in particular systematic cognitive …
detecting and being responsive to human cognitive states, in particular systematic cognitive …
Investigating methods for cognitive workload estimation for assistive robots
Robots interacting with humans in assistive contexts have to be sensitive to human cognitive
states to be able to provide help when it is needed and not overburden the human when the …
states to be able to provide help when it is needed and not overburden the human when the …
Cognitive workload assessment via eye gaze and eeg in an interactive multi-modal driving task
Assessing the cognitive workload of human interactants in mixed-initiative teams is a critical
capability for autonomous interactive systems to enable adaptations that improve team …
capability for autonomous interactive systems to enable adaptations that improve team …
Classification of visual and non-visual learners using electroencephalographic alpha and gamma activities
This study analyzes the learning styles of subjects based on their electroencephalo-graphy
(EEG) signals. The goal is to identify how the EEG features of a visual learner differ from …
(EEG) signals. The goal is to identify how the EEG features of a visual learner differ from …
A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces
O Aydemir, E Ergün - Journal of neuroscience methods, 2019 - Elsevier
Background The input signals of electroencephalography (EEG) based brain computer
interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However …
interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However …
SPECTRA: a tool for enhanced brain wave signal recognition
Background Brain wave signal recognition has gained increased attention in neuro-
rehabilitation applications. This has driven the development of brain–computer interface …
rehabilitation applications. This has driven the development of brain–computer interface …
Reconfiguration patterns of large-scale brain networks in motor imagery
Motor imagery (MI) is a multidimensional cognitive ability which recruited multiple brain
networks. However, how connections and interactions are adjusted among distributed …
networks. However, how connections and interactions are adjusted among distributed …
EEG-based BMI using multi-class motor imagery for bionic arm
AC Subrata, MA Riyadi… - 2020 3rd International …, 2020 - ieeexplore.ieee.org
One of the rehabilitation methods developed to help people with physical disabilities,
especially patients with disabilities of the upper-limb arm, to do Activities of Daily Living …
especially patients with disabilities of the upper-limb arm, to do Activities of Daily Living …