Article
Chemistry, Multidisciplinary
Maria Semeli Frangopoulou, Maryam Alimardani
Summary: This study compared the effectiveness of FFT-based spectral analysis and functional connectivity analysis for diagnosing Alzheimer's disease. The results showed that functional connectivity analysis outperformed spectral analysis, with higher synchronization levels in the AD group in the lower frequency bands, suggesting a "phase-locked" state in the affected brains.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Christoph Helmstaedter, Thorsten Rings, Lara Buscher, Benedikt Janssen, Sara Alaeddin, Vanessa Krause, Stefan Knecht, Klaus Lehnertz
Summary: This study successfully differentiated the brain network characteristics of patients with unresponsive wakefulness syndrome through a combination of EEG recordings, basal stimulation, and daily behavioral assessment, and showed the short-term and potential long-term recovery effects of personalized therapy.
SCIENTIFIC REPORTS
(2022)
Article
Behavioral Sciences
Adam Gyulai, Janos Kormendi, Mohamed F. Issa, Zoltan Juhasz, Zoltan Nagy
Summary: This study examined the motor-related bioelectric brain activity in healthy young and old subjects to understand the impact of aging on motor execution. The results showed that elderly individuals involved more nonmotor, parietal-occipital, and frontal areas in their motor networks, with higher global and local efficiency and node strength, while younger individuals exhibited decreased ERSP and ITC measures. Therefore, changes in ERSP and ITC may serve as sensitive and complementary biomarkers of age-related motor execution.
BRAIN AND BEHAVIOR
(2023)
Article
Mathematics, Applied
Ralph G. Andrzejak, Anais Espinoso, Eduardo Garcia-Portugues, Arthur Pewsey, Jacopo Epifanio, Marc G. Leguia, Kaspar Schindler
Summary: The article introduces how to quantify the concentration of unimodal circular data around the mean direction using the mean resultant length, and proposes a re-normalized version as an improvement. The relevance and effectiveness of the proposed method are illustrated through examples.
Article
Chemistry, Analytical
Renata Plucinska, Konrad Jedrzejewski, Urszula Malinowska, Jacek Rogala
Summary: Most studies on EEG-based biometry recognition use limited recorded EEG sessions for training and testing, which can lead to overestimated assessments. Our study shows that using multiple recording sessions for training improves sensitivity in EEG-based verification. Increasing the number of sessions above eight does not enhance results. Including data from multiple recording sessions is necessary for accurate EEG-based recognition.
Article
Physiology
Ileana Pirovano, Alfonso Mastropietro, Yuri Antonacci, Chiara Bara, Eleonora Guanziroli, Franco Molteni, Luca Faes, Giovanna Rizzo
Summary: The study of resting-state motor network functional connectivity using EEG is crucial in investigating changes that occur after an ischemic stroke and their correlation with motor function recovery.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Neurosciences
Ling Li, Xianshuo Wang, Jiahui Li, Yanping Zhao
Summary: This study proposes a fusion feature called P-MSWC to construct brain functional connectivity matrices and utilize convolutional neural network (CNN) to identify Major Depressive Disorder (MDD) based on electroencephalogram (EEG) signal. The proposed method achieves high accuracy in detecting MDD and outperforms traditional machine learning methods.
COGNITIVE NEURODYNAMICS
(2023)
Article
Engineering, Multidisciplinary
Rabiye Kilic, Nida Kumbasar, Emin Argun Oral, Ibrahim Yucel Ozbek
Summary: This study introduces a method for drone detection and classification using Radio Frequency (RF) signals and basic machine learning methods. The proposed method involves feature extraction and model training/testing stages. Experimental results demonstrate that the proposed method outperforms existing results on the DroneRF dataset.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Automation & Control Systems
Qingqing Zheng, Yi Wang, Pheng Ann Heng
Summary: This article introduces a tensor-based multitask learning method for joint feature selection and classification, leveraging shared structural information and task-specific information to improve model performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Clinical Neurology
Omid Sefat, Mohammad Ali Salehinejad, Marlon Danilewitz, Reza Shalbaf, Fidel Vila-Rodriguez
Summary: This study investigated the effect of combining yoga and tDCS on brain oscillations and networks. The results indicated that adding active tDCS to yoga leads to increased functional connectivity in the frontal area of the scalp and source EEG data.
Article
Neurosciences
Elisa Tatti, Francesca Ferraioli, Alberto Cacciola, Cameron Chan, Angelo Quartarone, Maria Felice Ghilardi
Summary: Modulation of gamma oscillations in the human motor cortex plays a crucial role in movement execution, with different gamma peak frequencies and topographies in the planning and execution phases. The amplitude of gamma synchronization predicts peak velocity and movement time, suggesting its role in selecting and controlling kinematic parameters.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Mohamad Amin Bakhshali, Morteza Khademi, Abbas Ebrahimi-Moghadam
Summary: This paper presents a framework for imagined speech recognition based on EEG signals and introduces a new EEG-based feature extraction method. Results show significant differences between imagined speech and baseline connectivity patterns, and the proposed method outperforms competing methods in terms of accuracy.
DIGITAL SIGNAL PROCESSING
(2022)
Review
Neurosciences
Michael Hen Forbord Fischer, Ivan Chrilles Zibrandtsen, Peter Hogh, Christian Sandoe Musaeus
Summary: This article systematically reviewed the literature on changes in electroencephalography (EEG) magnitude-squared coherence (MSCOH) in patients with Alzheimer's disease (AD), and found that alpha coherence was significantly reduced in AD patients, which may serve as a diagnostic marker for AD. However, further research is needed to validate the diagnostic utility of MSCOH.
JOURNAL OF ALZHEIMERS DISEASE
(2023)
Article
Neurosciences
Wupadrasta Santosh Kumar, Supratim Ray
Summary: Functional connectivity (FC) measures the interdependence of brain signals from different spatial locations and frequency bands, and is influenced by cognitive tasks, ageing, and cognitive disorders. Recent studies have shown a reduction in narrow-band gamma oscillations induced by visual gratings in both healthy ageing and subjects with mild cognitive impairment (MCI), but the impact of ageing/MCI on stimulus-induced gamma FC remains unclear. To investigate this, EEG was recorded from a large cohort of elderly subjects while they viewed large cartesian gratings. The results revealed distinct differences in power and FC across age and MCI groups, with decreased alpha FC in healthy ageing and significantly reduced gamma FC in MCI compared to age and gender matched controls, even when power was matched between groups. These findings suggest different underlying mechanisms for ageing and cognitive disorders.
EUROPEAN JOURNAL OF NEUROSCIENCE
(2023)
Article
Behavioral Sciences
Johanna Wind, Fabian Horst, Nikolas Rizzi, Alexander John, Tamara Kurti, Wolfgang I. Schoellhorn
Summary: Most neurophysiological dance research has focused on female participants in observational studies. This study explores the acute impact of physically executed modern jazz dance on brain activity and functional connectivity, finding gender differences in imagining dance without music but not in physically dancing or with music.
FRONTIERS IN BEHAVIORAL NEUROSCIENCE
(2021)