LAUSR

Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation

Sign Up to like & get
recommendations!
0 Published in 2018 at "Journal of Signal Processing Systems"

DOI: 10.1007/s11265-016-1164-z

Abstract: Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work,… read more here.

Keywords: tensor factorisation; eeg data; classification; dimensionality reduction ... See more keywords

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Sign Up to like & get
recommendations!
1 Published in 2022 at "Developmental Cognitive Neuroscience"

DOI: 10.1016/j.dcn.2022.101094

Abstract: Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is… read more here.

Keywords: time; analysis; eeg data; infant ... See more keywords

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data

Sign Up to like & get
recommendations!
1 Published in 2021 at "EBioMedicine"

DOI: 10.1016/j.ebiom.2021.103275

Abstract: Background Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity… read more here.

Keywords: artificial intelligence; seizure detection; eeg data; system ... See more keywords

Standardized music therapy with and without acclimatization, to improve EEG data acquisition in young children with and without disability

Sign Up to like & get
recommendations!
0 Published in 2019 at "Journal of Neuroscience Methods"

DOI: 10.1016/j.jneumeth.2019.02.013

Abstract: INTRODUCTION In young children, EEG data acquisition during stimulation tasks is difficult due to anxiety, movement and behaviorally-related interruptions, especially in those with disabilities. NEW METHOD We used standardized music therapy (MT) protocols with and… read more here.

Keywords: eeg data; acquisition; standardized music; young children ... See more keywords

Reply to “Separating neuroethics from neurohype”

Sign Up to like & get
recommendations!
0 Published in 2019 at "Nature Biotechnology"

DOI: 10.1038/s41587-019-0226-8

Abstract: Ienca et al. reply — In her response to our Commentary in the September 2018 issue1, Wexler makes incorrect statements on factual issues, misrepresents our analysis, and suggests a perspective on the (neuro)ethical debate that… read more here.

Keywords: eeg; research; eeg data; consumer ... See more keywords

Cross-Subject EEG Signal Recognition Using Deep Domain Adaptation Network

Sign Up to like & get
recommendations!
1 Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2939288

Abstract: Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other… read more here.

Keywords: eeg signal; eeg data; deep domain; recognition ... See more keywords

Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification

Sign Up to like & get
recommendations!
1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3188286

Abstract: Sleep EEG signals analysis is an approach that helps researchers identify and understand the different phenomena concealed within sleep EEG data. This research introduces a time-frequency analysis approach to untangle the parameters of the sleep… read more here.

Keywords: sleep eeg; stages classification; eeg data; eeg ... See more keywords

Inference on Long-Range Temporal Correlations in Human EEG Data

Sign Up to like & get
recommendations!
1 Published in 2020 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2019.2936326

Abstract: Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal… read more here.

Keywords: eeg data; range temporal; dfa exponent; long range ... See more keywords

AsEmo: Automatic Approach for EEG-Based Multiple Emotional State Identification

Sign Up to like & get
recommendations!
0 Published in 2021 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2020.3032678

Abstract: An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to… read more here.

Keywords: eeg data; asemo; state; eeg ... See more keywords

An Edge-Fog Computing-Enabled Lossless EEG Data Compression With Epileptic Seizure Detection in IoMT Networks

Sign Up to like & get
recommendations!
1 Published in 2022 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3143704

Abstract: The need to improve smart health systems to monitor the health situation of patients has grown as a result of the spread of epidemic diseases, the ageing of the population, the increase in the number… read more here.

Keywords: epileptic seizure; eeg data; tex math; inline formula ... See more keywords

Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems

Sign Up to like & get
recommendations!
1 Published in 2022 at "IEEE Transactions on Information Forensics and Security"

DOI: 10.1109/tifs.2022.3204222

Abstract: As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research… read more here.

Keywords: template design; eeg data; privacy; eeg ... See more keywords