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 …
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 …
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 …
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
GC Suguna, ST Veerabhadrappa, A Tejas… - EMITTER …, 2022 - emitter.pens.ac.id
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 …