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 …
Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
A review of machine learning-based human activity recognition for diverse applications
Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …
computer science. Due to the articulated nature of human motion, it is not trivial to detect …
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …
on brain signals aims to discover the underlying neurological or physical status of the …
Multiday EMG-based classification of hand motions with deep learning techniques
Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …
performance of myoelectric control for upper limb prostheses with respect to current clinical …
[PDF][PDF] A survey on deep learning based brain computer interface: Recent advances and new frontiers
Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …
by decoding individuals' brain signals into commands recognizable by computer devices …
Sentiment analysis of before and after elections: Twitter data of us election 2020
US President Joe Biden took his oath after being victorious in the controversial US elections
of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic …
of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic …
Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold
This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG
signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold …
signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold …
[PDF][PDF] Bio-signals Compression Using Auto Encoder
KN Sunil Kumar, D Shivashankar… - Journal of Electrical …, 2021 - academia.edu
Latest developments in wearable devices permits un-damageable and cheapest way for
gathering of medical data such as bio-signals like ECG, Respiration, Blood pressure etc …
gathering of medical data such as bio-signals like ECG, Respiration, Blood pressure etc …