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Now showing items 11-20 of 24
Bayesian network based heuristic for energy aware EEG signal classification
(
SpringerLink
, 2013 , Conference Paper)
A major challenge in the current research of wireless electroencephalograph (EEG) sensor-based medical or Brain Computer Interface applications is how to classify EEG signals as accurately and energy efficient as possible. ...
Adaptive energy-aware encoding for DWT-based wireless EEG tele-monitoring system
(
IEEE Computer Society
, 2013 , Conference Paper)
Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography ...
Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network
(
Elsevier Ltd
, 2015 , Article)
Recent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless ...
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs
(
Elsevier Ltd
, 2022 , Article)
Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital ...
Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure ...
Long-term epileptic EEG classification via 2D mapping and textural features
(
Elsevier Ltd
, 2015 , Article)
Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic ...
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other ...
Sleep stage classification using sparse rational decomposition of single channel EEG records
(
Institute of Electrical and Electronics Engineers Inc.
, 2015 , Conference Paper)
A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands ...
Time-frequency image descriptors-based features for EEG epileptic seizure activities detection and classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2015 , Conference Paper)
This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived ...
Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing
(2012 , Article)
This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to ...