A review of the filtering techniques used in EEG signal processing
D Sen, BB Mishra, PK Pattnaik - 2023 7th International …, 2023 - ieeexplore.ieee.org
In this paper, a general overview of the different kinds of filters, their applications in real
world and the various pitfalls of filtering have been briefly discussed with a special focus on …
world and the various pitfalls of filtering have been briefly discussed with a special focus on …
Biot: Biosignal transformer for cross-data learning in the wild
Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
Deep-learning-based BCI for automatic imagined speech recognition using SPWVD
The electroencephalogram (EEG)-based brain–computer interface (BCI) has potential
applications in neuroscience and rehabilitation. It benefits a person with neurological …
applications in neuroscience and rehabilitation. It benefits a person with neurological …
RISC-V CNN coprocessor for real-time epilepsy detection in wearable application
SY Lee, YW Hung, YT Chang, CC Lin… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Epilepsy is a common clinical disease. Severe epilepsy can be life-threatening in certain
unexpected conditions, so it is important to detect seizures instantly with a wearable device …
unexpected conditions, so it is important to detect seizures instantly with a wearable device …
BIOT: Cross-data biosignal learning in the wild
Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments
The evolution of different techniques for exploring cerebral activity and the development of
signal processing and analysis methods have enabled a better understanding of the …
signal processing and analysis methods have enabled a better understanding of the …
Deep learning approach for EEG artifact identification and classification
R Rajabioun, AÖ Akyürek… - 2021 6th International …, 2021 - ieeexplore.ieee.org
Electroencephalography (EEG) signals are normally susceptible to various artifacts and
noises from different sources. In this paper, firstly the existence of artifacts will be identified …
noises from different sources. In this paper, firstly the existence of artifacts will be identified …
A Comprehensive Survey on Rehabilitative Applications of Electroencephalogram in Healthcare
A set of therapeutic control required for persons suffering from or expected to suffer from
limitations in daily living activities is called rehabilitation which can restore or improve the …
limitations in daily living activities is called rehabilitation which can restore or improve the …
Analysis of diabetes patients using classification algorithms
JJ Abinas, HVK Chandolu… - 2021 10th IEEE …, 2021 - ieeexplore.ieee.org
Medical applications find classification algorithms for analyzing the patient's condition.
Classification algorithms in turn are of many types and deals with variety of characteristics of …
Classification algorithms in turn are of many types and deals with variety of characteristics of …
A Machine learning Classification approach for detection of Covid 19 using CT images
Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in
December 2019. World Health Organization declared Covid 19 as a transmission disease …
December 2019. World Health Organization declared Covid 19 as a transmission disease …