Objective: The aim of this study was to analyse the regularity of the EEG background activity of Alzheimer's disease (AD) patients to test the hypothesis that the irregularity of the AD patients' EEG is lower than that of age-matched controls.
Methods: We recorded the EEG from 19 scalp electrodes in 10 AD patients and 8 age-matched controls and estimated the Approximate Entropy (ApEn). ApEn is a non-linear statistic that can be used to quantify the irregularity of a time series. Larger values correspond to more complexity or irregularity. A spectral analysis was also performed.
Results: ApEn was significantly lower in the AD patients at electrodes P3 and P4 (P < 0.01), indicating a decrease of irregularity. We obtained 70% sensitivity and 100% specificity at P3, and 80% sensitivity and 75% specificity at P4. Results seemed to be complementary to spectral analysis.
Conclusions: The decreased irregularity found in the EEG of AD patients in the parietal region leads us to think that EEG analysis with ApEn could be a useful tool to increase our insight into brain dysfunction in AD. However, caution should be applied due to the small sample size.
Significance: This article represents a first step in demonstrating the feasibility of ApEn for recognition of EEG changes in AD.
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http://dx.doi.org/10.1016/j.clinph.2005.04.001 | DOI Listing |
Cogn Neurodyn
December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.
View Article and Find Full Text PDFFront Hum Neurosci
January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
It has been classically conjectured that the brain assigns probabilistic models to sequences of stimuli. An important issue associated with this conjecture is the identification of the classes of models used by the brain to perform this task. We address this issue by using a new clustering procedure for sets of electroencephalographic (EEG) data recorded from participants exposed to a sequence of auditory stimuli generated by a stochastic chain.
View Article and Find Full Text PDFBrain Commun
December 2024
Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, 1011 Lausanne, Switzerland.
A key question for the scientific study of consciousness is whether it is possible to identify specific features in brain activity that are uniquely linked to conscious experience. This question has important implications for the development of markers to detect covert consciousness in unresponsive patients. In this regard, many studies have focused on investigating the neural response to complex auditory regularities.
View Article and Find Full Text PDFCortex
December 2024
Department of Psychological Sciences, University of Liverpool, Liverpool, UK.
The human visual system is tuned to symmetry, and the neural response to visual symmetry has been well studied. One line of research measures an Event Related Potential (ERP) component called the Sustained Posterior Negativity (SPN). Amplitude is more negative at posterior electrodes when participants see symmetrical patterns compared to asymmetrical patterns.
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