Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks
MHR Rabbani, SMR Islam - Cognitive Neurodynamics, 2023 - Springer
The detection of the cognitive tasks performed by a subject during data acquisition of a
neuroimaging method has a wide range of applications: functioning of brain-computer …
neuroimaging method has a wide range of applications: functioning of brain-computer …
Multimodal decision fusion of eeg and fnirs signals
MHR Rabbani, SMR Islam - 2021 5th International Conference …, 2021 - ieeexplore.ieee.org
Fusion of electroencephalography (EEG) as a physiological signal and functional near-
infrared spectroscopy (fNIRS) as a metabolic signal has enormous potentials in different …
infrared spectroscopy (fNIRS) as a metabolic signal has enormous potentials in different …
Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals
Brain–computer interface (BCI) is a powerful system for communicating between the brain
and outside world. Traditional BCI systems work based on electroencephalogram (EEG) …
and outside world. Traditional BCI systems work based on electroencephalogram (EEG) …
Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder
A Güven, M Altınkaynak, N Dolu, M İzzetoğlu… - Neural Computing and …, 2020 - Springer
Recently multimodal neuroimaging which combines signals from different brain modalities
has started to be considered as a potential to improve the accuracy of diagnosis. The current …
has started to be considered as a potential to improve the accuracy of diagnosis. The current …
Classification algorithm for fNIRS-based brain signals using convolutional neural network with spatiotemporal feature extraction mechanism
Y Qin, B Li, W Wang, X Shi, C Peng, Y Lu - Neuroscience, 2024 - Elsevier
Abstract Brain Computer Interface (BCI) is a highly promising human–computer interaction
method that can utilize brain signals to control external devices. BCI based on functional …
method that can utilize brain signals to control external devices. BCI based on functional …
Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer …
Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature
extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning …
extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning …
Multimodal fNIRS-EEG classification using deep learning algorithms for brain-computer interfaces purposes
The development of brain-computer interface (BCI) systems has received considerable
attention from neuroscientists in recent years. BCIs can serve as a means of communication …
attention from neuroscientists in recent years. BCIs can serve as a means of communication …
EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN
Analysis of brain signals is essential to the study of mental states and various neurological
conditions. The two most prevalent noninvasive signals for measuring brain activities are …
conditions. The two most prevalent noninvasive signals for measuring brain activities are …
Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network
Practical brain–computer interface (BCI) demands the learning-based adaptive model that
can handle diverse problems. To implement a BCI, usually functional near-infrared …
can handle diverse problems. To implement a BCI, usually functional near-infrared …
Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework
RJ Deligani, SB Borgheai, J McLinden… - Biomedical optics …, 2021 - opg.optica.org
Multimodal data fusion is one of the current primary neuroimaging research directions to
overcome the fundamental limitations of individual modalities by exploiting complementary …
overcome the fundamental limitations of individual modalities by exploiting complementary …