Security in brain-computer interfaces: state-of-the-art, opportunities, and future challenges
Brain-Computer Interfaces (BCIs) have significantly improved the patients' quality of life by
restoring damaged hearing, sight, and movement capabilities. After evolving their …
restoring damaged hearing, sight, and movement capabilities. After evolving their …
[HTML][HTML] Wearable electroencephalography and multi-modal mental state classification: A systematic literature review
Background: Wearable multi-modal time-series classification applications outperform their
best uni-modal counterparts and hold great promise. A modality that directly measures …
best uni-modal counterparts and hold great promise. A modality that directly measures …
Soft computing-based EEG classification by optimal feature selection and neural networks
Brain computer interface translates electroencephalogram (EEG) signals into control
commands so that paralyzed people can control assistive devices. This human thought …
commands so that paralyzed people can control assistive devices. This human thought …
Enhanced detection of epileptic seizure using EEG signals in combination with machine learning classifiers
W Mardini, MMB Yassein, R Al-Rawashdeh… - IEEE …, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) is one of the most powerful tools that offer valuable
information related to different abnormalities in the human brain. One of these abnormalities …
information related to different abnormalities in the human brain. One of these abnormalities …
Multiclass EEG motor-imagery classification with sub-band common spatial patterns
Electroencephalogram (EEG) signal classification plays an important role to facilitate
physically impaired patients by providing brain-computer interface (BCI)-controlled devices …
physically impaired patients by providing brain-computer interface (BCI)-controlled devices …
Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models
Epilepsy is a neurological condition that affects the central nervous system. While its effects
are different for each person, they mostly include abnormal behaviour, periods of loss of …
are different for each person, they mostly include abnormal behaviour, periods of loss of …
Analysis of factors that influence the performance of biometric systems based on EEG signals
D Carrión-Ojeda, R Fonseca-Delgado… - Expert Systems with …, 2021 - Elsevier
Searching for new biometric traits is currently a necessity because traditional ones such as
fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric …
fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric …
A review on machine learning approaches in identification of pediatric epilepsy
Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of
the brain which results in recurrent seizures. Though the epilepsy in general is considered …
the brain which results in recurrent seizures. Though the epilepsy in general is considered …
Brain signals analysis based deep learning methods: Recent advances in the study of non-invasive brain signals
A Essa, H Kotte - arXiv preprint arXiv:2201.04229, 2021 - arxiv.org
Brain signals constitute the information that are processed by millions of brain neurons
(nerve cells and brain cells). These brain signals can be recorded and analyzed using …
(nerve cells and brain cells). These brain signals can be recorded and analyzed using …
Deep learning classification of two-class motor imagery EEG signals using transfer learning
N Shajil, M Sasikala… - … Conference on e-Health …, 2020 - ieeexplore.ieee.org
Motor imagery (MI) based Brain-Computer Interface (BCI) system uses
Electroencephalography (EEG) signals recorded over the scalp during imagination of motor …
Electroencephalography (EEG) signals recorded over the scalp during imagination of motor …