Feature attraction and classification of mental EEG using approximate entropy.

Conf Proc IEEE Eng Med Biol Soc

College of Information Science & Engineering, Shandong University,P.R. China.

Published: September 2008

The approximate entropy (ApEn), which is a new statistical method to measure the complexity of sequences, was introduced in this paper. First, the EOG artifact was removed from the EEG using the method of independent component analysis (ICA). Then ApEn was used to analyze the mental EEG signals to extract the features for pattern identification and task classification. The simulations showed that the classification accuracy is high and the proposed methods are effective.

Download full-text PDF

Source
http://dx.doi.org/10.1109/IEMBS.2005.1615852DOI Listing

Publication Analysis

Top Keywords

mental eeg
8
approximate entropy
8
feature attraction
4
attraction classification
4
classification mental
4
eeg approximate
4
entropy approximate
4
entropy apen
4
apen statistical
4
statistical method
4

Similar Publications

The aim of the study was to evaluate the concomitant psychiatric disorders of anxiety and depression in patients with epilepsy caused by low-grade brain tumors (LBTs). We retrospectively reviewed the clinical data of patients who underwent preoperative neuropsychological evaluations of anxiety and depression and subsequent epilepsy surgery for LBTs. The univariate and multivariate analyses were conducted to analyze the risk factors of the occurrence of anxiety and depression.

View Article and Find Full Text PDF

Individualized Spectral Features in First-episode and Drug-naïve Major Depressive Disorder: Insights from Periodic and Aperiodic EEG Analysis.

Biol Psychiatry Cogn Neurosci Neuroimaging

January 2025

School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address:

Background: The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences.

Methods: This study aimed to elucidate the pathologic changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD.

View Article and Find Full Text PDF

Objectives: To build an early, prognostic model for adverse outcome in infants with hypoxic ischemic encephalopathy (HIE) receiving therapeutic hypothermia (TH) based on brain magnetic resonance images (MRI), electrophysiological tests and clinical assessments were performed during the first 5 days of life.

Methods: Retrospective study of 182 neonates with HIE and managed with TH. The predominant pattern of HIE brain injury on MRI performed following cooling was scored by neuroradiologists.

View Article and Find Full Text PDF

Biomarkers.

Alzheimers Dement

December 2024

National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.

Background: Growing evidence suggests that the imbalance between excitability and inhibitory neural activity is a key aspect of cognitive decline. Subclinical epileptiform activity (SEA) has been indicated as a marker of increased cortical excitability. While SEA is considered as a benign EEG sign in the elderly population, recent studies demonstrated its role in the progression of Alzheimer's disease.

View Article and Find Full Text PDF

Biomarkers.

Alzheimers Dement

December 2024

University of Minnesota, Minneapolis, MN, USA.

Background: There is ample evidence that music can boost brain activity and jog deeply embedded memories. Literature indicates a significant improvement in autobiographical memory (ABM) recall for different individuals during background music sessions. Existing research is based solely on qualitative data, although music has a significant impact on physiological activity.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!