[HTML][HTML] A review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection
K Akyol - Expert Systems with Applications, 2020 - Elsevier
Electroencephalography signals obtained from the brain's electrical activity are commonly
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
Automatic and non-invasive Parkinson's disease diagnosis and severity rating using LSTM network
Deep learning has a huge potential in healthcare for uncovering the hidden patterns from
large volume of clinical data to diagnose different diseases. This paper presents a novel …
large volume of clinical data to diagnose different diseases. This paper presents a novel …
EEG-ConvTransformer for single-trial EEG-based visual stimulus classification
S Bagchi, DR Bathula - Pattern Recognition, 2022 - Elsevier
Different categories of visual stimuli evoke distinct activation patterns in the human brain.
These patterns can be captured with EEG for utilization in application such as Brain …
These patterns can be captured with EEG for utilization in application such as Brain …
CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning
In this paper, we propose a new technique that applies automated image analysis in the
area of structural corrosion monitoring and demonstrate improved efficacy compared to …
area of structural corrosion monitoring and demonstrate improved efficacy compared to …
DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image
How to design a suitable model to extract the semantic features contained in
Electroencephalography (EEG) and to visualize them as corresponding images, also known …
Electroencephalography (EEG) and to visualize them as corresponding images, also known …
Early detection of myocardial ischemia in 12‐lead ECG using deterministic learning and ensemble learning
Q Sun, C Liang, T Chen, B Ji, R Liu, L Wang… - Computer Methods and …, 2022 - Elsevier
Background and objective: Early detection of myocardial ischemia is a necessary but difficult
problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T …
problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T …
Direct-sense brain–computer interfaces and wearable computers
Brain-computer interfaces (BCIs) allow users to communicate directly with external devices
via their brain signals. Recently, BCIs, and wearable computers in particular, have been …
via their brain signals. Recently, BCIs, and wearable computers in particular, have been …
[HTML][HTML] A novel ensemble CNN model for COVID-19 classification in computerized tomography scans
LF de Jesus Silva, OAC Cortes, JOB Diniz - Results in Control and …, 2023 - Elsevier
COVID-19 is a rapidly spread infectious disease caused by a severe acute respiratory
syndrome that can lead to death in just a few days. Thus, early disease detection can …
syndrome that can lead to death in just a few days. Thus, early disease detection can …
[HTML][HTML] Self-supervised cross-modal visual retrieval from brain activities
We study the problem of restoring visual stimuli from visually-evoked
electroencephalography (EEG) signals. Using a supervised classification-then-generation …
electroencephalography (EEG) signals. Using a supervised classification-then-generation …