Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
[PDF][PDF] Study of variants of extreme learning machine (ELM) brands and its performance measure on classification algorithm
JS Manoharan - Journal of Soft Computing Paradigm (JSCP), 2021 - scholar.archive.org
Recently, the feed-forward neural network is functioning with slow computation time and
increased gain. The weight vector and biases in the neural network can be tuned based on …
increased gain. The weight vector and biases in the neural network can be tuned based on …
Non-iterative and fast deep learning: Multilayer extreme learning machines
In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …
and drawn ever-increasing research interests. However, conventional deep learning …
DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
Depression is a psychological disorder characterized by the continuous occurrence of bad
mood state. It is critical to understand that this disorder is severely affecting people of …
mood state. It is critical to understand that this disorder is severely affecting people of …
Automated depression detection using deep representation and sequence learning with EEG signals
Depression affects large number of people across the world today and it is considered as
the global problem. It is a mood disorder which can be detected using …
the global problem. It is a mood disorder which can be detected using …
Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition
Soil moisture (SM) is an essential component of the environmental and the agricultural
system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to …
system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to …
Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the
design and development of gas and oil field plays. It plays an essential role in the selection …
design and development of gas and oil field plays. It plays an essential role in the selection …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
The explosive growth of data in volume, velocity and diversity that are produced by medical
applications has contributed to abundance of big data. Current solutions for efficient data …
applications has contributed to abundance of big data. Current solutions for efficient data …