Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces
In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
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 …
Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling
SH Wang, P Phillips, Y Sui, B Liu, M Yang… - Journal of medical …, 2018 - Springer
Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide
a new computer-vision based technique to detect it in an efficient way. The brain-imaging …
a new computer-vision based technique to detect it in an efficient way. The brain-imaging …
Evaluation of neural degeneration biomarkers in the prefrontal cortex for early identification of patients with mild cognitive impairment: an fNIRS study
Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated
with aging and depicts early symptoms of severe cognitive impairment, known as …
with aging and depicts early symptoms of severe cognitive impairment, known as …
Analyzing classification performance of fNIRS-BCI for gait rehabilitation using deep neural networks
This research presents a brain-computer interface (BCI) framework for brain signal
classification using deep learning (DL) and machine learning (ML) approaches on functional …
classification using deep learning (DL) and machine learning (ML) approaches on functional …
Deep learning a boon for biophotonics?
This review covers original articles using deep learning in the biophotonic field published in
the last years. In these years deep learning, which is a subset of machine learning mostly …
the last years. In these years deep learning, which is a subset of machine learning mostly …
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 …
Transformer model for functional near-infrared spectroscopy classification
Z Wang, J Zhang, X Zhang, P Chen… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The
fNIRS classification problem has always been the focus of the brain-computer interface …
fNIRS classification problem has always been the focus of the brain-computer interface …
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 …