Deep learning in fNIRS: a review
Significance: Optical neuroimaging has become a well-established clinical and research
tool to monitor cortical activations in the human brain. It is notable that outcomes of …
tool to monitor cortical activations in the human brain. It is notable that outcomes of …
Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges
MH Alkinani, WZ Khan, Q Arshad - Ieee Access, 2020 - ieeexplore.ieee.org
Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
FGANet: fNIRS-guided attention network for hybrid EEG-fNIRS brain-computer interfaces
Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding,
linking neural signals to control devices. Hybrid BCI systems using electroencephalography …
linking neural signals to control devices. Hybrid BCI systems using electroencephalography …
Systemic review on transcranial electrical stimulation parameters and EEG/fNIRS features for brain diseases
Background Brain disorders are gradually becoming the leading cause of death worldwide.
However, the lack of knowledge of brain disease's underlying mechanisms and ineffective …
However, the lack of knowledge of brain disease's underlying mechanisms and ineffective …
Early identification and detection of driver drowsiness by hybrid machine learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …
Enhanced accuracy for multiclass mental workload detection using long short-term memory for brain–computer interface
Cognitive workload is one of the widely invoked human factors in the areas of human–
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …
[HTML][HTML] Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction
K Barnova, M Mikolasova, RV Kahankova… - Computers in Biology …, 2023 - Elsevier
Brain–computer interfaces are used for direct two-way communication between the human
brain and the computer. Brain signals contain valuable information about the mental state …
brain and the computer. Brain signals contain valuable information about the mental state …
Detection of mild cognitive impairment using convolutional neural network: temporal-feature maps of functional near-infrared spectroscopy
Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which
is considered the most common neurodegenerative disease in the elderly. Some MCI …
is considered the most common neurodegenerative disease in the elderly. Some MCI …
Hybrid EEG-fNIRS brain computer interface based on common spatial pattern by using EEG-informed general linear model
Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …
Passive brain-computer interfaces for enhanced human-robot interaction
M Alimardani, K Hiraki - Frontiers in Robotics and AI, 2020 - frontiersin.org
Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate
changes in brain activity, produced either by means of a volitional modulation or in response …
changes in brain activity, produced either by means of a volitional modulation or in response …