Deep learning for detecting multi-level driver fatigue using physiological signals: A comprehensive approach
A large share of traffic accidents is related to driver fatigue. In recent years, many studies
have been organized in order to diagnose and warn drivers. In this research, a new …
have been organized in order to diagnose and warn drivers. In this research, a new …
Automatic detection of driver fatigue based on EEG signals using a developed deep neural network
In recent years, detecting driver fatigue has been a significant practical necessity and issue.
Even though several investigations have been undertaken to examine driver fatigue, there …
Even though several investigations have been undertaken to examine driver fatigue, there …
Visual saliency and image reconstruction from EEG signals via an effective geometric deep network-based generative adversarial network
Reaching out the function of the brain in perceiving input data from the outside world is one
of the great targets of neuroscience. Neural decoding helps us to model the connection …
of the great targets of neuroscience. Neural decoding helps us to model the connection …
Automatically identified EEG signals of movement intention based on CNN network (End-To-End)
Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic
identification of movement intent. They also allow patients with motor disorders to …
identification of movement intent. They also allow patients with motor disorders to …
[HTML][HTML] Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
S Sheykhivand, Z Mousavi, S Mojtahedi… - Alexandria Engineering …, 2021 - Elsevier
Abstract The novel coronavirus (COVID-19) could be described as the greatest human
challenge of the 21st century. The development and transmission of the disease have …
challenge of the 21st century. The development and transmission of the disease have …
Mixed reality-based brain computer interface system using an adaptive bandpass filter: Application to remote control of mobile manipulator
Q Li, M Sun, Y Song, D Zhao, T Zhang, Z Zhang… - … Signal Processing and …, 2023 - Elsevier
Brain-computer interface (BCI) systems based on mixed reality (MR) have promising
applications in assisting people with disabilities to control manipulators. Using MR glasses …
applications in assisting people with disabilities to control manipulators. Using MR glasses …
PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease
NH Kim, U Park, DW Yang, SH Choi, YC Youn… - Scientific Reports, 2023 - nature.com
Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and
monitoring its progression. Although EEG is non-invasive direct measurement of brain …
monitoring its progression. Although EEG is non-invasive direct measurement of brain …
Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification
Identifying seizure activities in non-stationary electroencephalography (EEG) is a
challenging task since it is time-consuming, burdensome, and dependent on expensive …
challenging task since it is time-consuming, burdensome, and dependent on expensive …
Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network
S Ansari, AH Navin, A Babazadeh Sangar… - Electronics, 2023 - mdpi.com
A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of
cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all …
cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all …
Hybrid approach: combining ecca and sscor for enhancing ssvep decoding
Currently, steady-state visual evoked potentials (SSVEPs) are applied in a variety of fields.
In these applications, spatial filtering is the most commonly used method for decoding …
In these applications, spatial filtering is the most commonly used method for decoding …