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Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classification of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coefficients.
View Article and Find Full Text PDFBrain oscillatory patterns in mild cognitive impairment due to Alzheimer's and Parkinson's disease: An exploratory high-density EEG study.
Clin Neurophysiol
June 2022
Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Trieste University Hospital - ASUGI, University of Trieste, Strada di Fiume, 447 - 34149 Trieste, Italy. Electronic address:
Objective: We investigated brain cortical activity alterations, using a resting-state 256-channel high-density EEG (hd-EEG), in Alzheimer's (AD) and Parkinson's (PD) disease subjects with mild cognitive impairment (MCI) and correlations between quantitative spectral EEG parameters and the global cognitive status assessed by Montreal Cognitive Assessment (MoCA) score.
Methods: Fifteen AD-MCI, eleven PD-MCI and ten age-matched healthy-controls (HC) underwent hd-EEG recordings and neuropsychological assessment. Cerebrospinal fluid biomarker analysis was performed to obtain well-characterized groups.
High Frequential Resolution Networks: Considerations on a New Functional Brain Connectivity Framework.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band.
View Article and Find Full Text PDFA comparison between the Hilbert-Huang and Discrete Wavelet Transforms to recognize emotions from electroencephalographic signals.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
Article Synopsis
- Recent studies on emotion recognition from EEG signals typically use linear transformations, but EEG is actually non-linear and non-stationary, suggesting a need for more suitable methods.
- This research compares the effectiveness of a non-linear, non-stationary approach (Hilbert-Huang Transformation) with a linear method (Discrete Wavelet Transform) using data from 10 subjects.
- The findings show that the Hilbert-Huang Transformation significantly outperformed the Wavelet-based method in classifying emotions (P < 0.02), indicating its potential to improve emotion recognition systems beneficial for diagnosing mental health conditions like autism and depression.
Added Value of QEEG for the Differential Diagnosis of Common Forms of Dementia.
Clin EEG Neurosci
May 2021
Department of Neurosciences, 37576"Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Cluj, Romania.
Introduction: Quantitative electroencephalography (QEEG) has been documented as a helpful tool in the differential diagnosis of Alzheimer's disease (AD) with common forms of dementia. The main objective of the study was to assess the role of QEEG in AD differential diagnosis with other forms of dementia: Lewy body dementia (LBD), Parkinson's disease dementia (PDD), frontotemporal dementia (FTD), and vascular dementia (VaD).
Methods: We searched PubMed, Embase, and PsycNET, for articles in English published in peer-reviewed journals from January 1, 1980 to April 23, 2019 using adapted search strategies containing keywords and .
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