Brain state forecasting for precise brain stimulation: Current approaches and future perspectives by Matteo De Matola, Carlo Miniussi

“…We focus on the problem of online functional targeting, which requires collecting electroencephalography (EEG) data, extracting brain states, and using them to trigger TMS in real time. …”
Get full text

Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy by Jungho Suh, Sungbok Chang, Hyunjun Park

“…An EMDR field test with electroencephalography (EEG) was conducted to assess the optimized video stimuli’s efficacy. …”
Get full text

EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism by Jayoti Bansal, Gaurav Gangwar, Mohammad Aljaidi, Ali Alkoradees, Gagandeep Singh

“…Method: This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. …”
Get full text

Exploring the role of electrode density in capturing spatiotemporal dynamics of resting-state networks with EEG by Matheus Mangini Bertuzzo, Rodrigo P Rocha, Ricardo Spyrídes Boabaid Pimentel Gonçalves, Adair Roberto Soares Dos Santos, Odival Cezar Gasparotto

“…This study investigates the role of electrode density in capturing resting-state brain activity, an area of significant clinical relevance, where electroencephalography (EEG) is favored for its cost-efficiency. …”
Get full text

The Longitudinal Neurophysiological Adaptation of a Division I Female Lacrosse Player Following Anterior Cruciate Rupture and Repair: A Case Report by Robert Mangine, Jim Tersak, Thomas Palmer, Audrey Hill-Lindsay, Bolton Patton, Marsha Eifert-Mangine, Bradley Jacobs, Angelo J Colosimo

“… # Purpose To investigate the innovative use of quantitative electroencephalography (qEEG) to monitor the longitudinal change in brain and central nervous systems activity while measuring musculoskeletal function during an anterior cruciate ligament repair rehabilitation…”
Get full text

Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments by Zhihong Cui, Xiangwei Zheng, Xuexiao Shao, Lizhen Cui

“…In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. …”
Get full text

Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

“…By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. …”
Get full text

Hyperexcitability of Cortical Oscillations in Patients with Somatoform Pain Disorder: A Resting-State EEG Study by Qian Ye, Dong Yan, Manlin Yao, Wutao Lou, Weiwei Peng

“…The present study is aimed at identifying the abnormalities of spontaneous cortical oscillations among patients with SPD, thus for a better understanding of the ongoing brain states in these patients. Spontaneous electroencephalography data during a resting state with eyes open were recorded from SPD patients and healthy controls, and their cortical oscillations as well as functional connectivity were compared using both electrode-level and source-level analysis. …”
Get full text

XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…To explore the impact of stereograms with varied motions on brain activities, we collected Electroencephalography (EEG) signals evoked by Dynamic Random Dot Stereograms (DRDS). …”
Get full text

Assessing brain-muscle networks during motor imagery to detect covert command-following by Emilia Fló, Daniel Fraiman, Jacobo Diego Sitt

“…Abstract Background In this study, we evaluated the potential of a network approach to electromyography and electroencephalography recordings to detect covert command-following in healthy participants. …”
Get full text

Sensation seeking and risk adjustment: the role of reward sensitivity in dynamic risky decisions by Yin Qianlan, Chen Shou, Hou Tianya, Dong Wei, Taosheng Liu

“…We enlisted 80 young adults to perform this task, and of these, 40 were subjected to electroencephalography (EEG) to assess neural correlates of RS. …”
Get full text

Patient-Independent Epileptic Seizure Detection with Reduced EEG Channels and Deep Recurrent Neural Networks by Nadine El-Dajani, Tim Friedrich Lutz Wilhelm, Jan Baumann, Rainer Surges, Bernd T. Meyer

“…Epileptic seizures affect around 1% of people worldwide and have an enormous impact on the quality of life as well as the health of each patient. Electroencephalography (EEG) is widely used to diagnose epilepsy and detect seizures. …”
Get full text

Optical approaches for neurocritical care: Toward non-invasive recording of cerebral physiology in acute brain injury by Austen T. Lefebvre, Nicole E. Steiner, Carissa L. Rodriguez, Joseph P. Angelo, Eyal Bar-Kochba, Rohan Mathur, Marek Mirski, David W. Blodgett

“…Current established approaches for monitoring cerebral physiology include the neurologic physical examination, traditional brain imaging such as computed tomography (CT) and magnetic resonance imaging (MRI), electroencephalography (EEG), and bedside modalities such as invasive parenchymal probes and transcranial doppler ultrasound. …”
Get full text

A Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals by S. Saranya, R. Menaka

“…Early diagnosis and timely intervention can improve outcomes by enabling tailored therapeutic strategies. Electroencephalography (EEG) has emerged as a non-invasive tool to capture brain activity and facilitate the early detection of ASD using machine learning techniques. …”
Get full text

Task-Driven Neurophysiological qEEG Baseline Performance Capabilities in Healthy, Uninjured Division-I College Athletes. by Robert E. Mangine, Thomas G. Palmer, James A. Tersak, Michael Mark, Joseph F Clark, Marsha Eifert-Mangine, Audrey Hill-Lindsay, Brian M Grawe

“…Quantitative electroencephalography or qEEG brain mapping is a unique, real-time comprehensive assessment of brain electrical activity performed in combination with physiometrics which offers insight to neurophysiological brain-to-body function. …”
Get full text

Taking into account students' psychotypes and using their neuropsychological maps when implementing digital educational technologies within the Metaverse by Galiya Ldokova, Svetlana Frumina, Suad Abdalkareem Alwaely

“…The results of the psychodiagnostic testing were compared with the data of electroencephalography (EEG) neuroimaging. The following statistical methods were used to analyze the research results: Student's t-test, ANOVA analysis of variance, and Pearson's correlation coefficient. …”
Get full text

Phase-Amplitude Coupling of Neural Oscillations Can Be Effectively Probed with Concurrent TMS-EEG by Sarah Glim, Yuka O. Okazaki, Yumi Nakagawa, Yuji Mizuno, Takashi Hanakawa, Keiichi Kitajo

“…In a concurrent TMS-electroencephalography study, we therefore examined the technique’s influence on theta-gamma, alpha-gamma, and beta-gamma phase-amplitude coupling by delivering single-pulse TMS (sTMS) and repetitive TMS (rTMS) over the left motor cortex and right visual cortex of healthy participants. …”
Get full text

Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm by Gianluigi Giannini, Till Nierhaus, Felix Blankenburg

“…Therefore, the aim of the present study was twofold: first we aimed validating a novel VR paradigm during electroencephalography (EEG) recoding to investigate sensory attenuation in a highly controlled setup; second, we tested whether electrophysiological differences between self- and externally-generated sensations could be better explained by stimulus predictability factors, corroborating the validity of sensory attenuation. …”
Get full text

EEG-based multivariate and univariate analyses reveal the mechanisms underlying the recognition-based production effect: evidence from mixed-list design by Bohua Zhang, Bohua Zhang, Alhassan Abdullah, Minmin Yan, Yongqing Hou, Antao Chen, Helen McLaren

“…This study explored how reading aloud affects recollection and familiarity using electroencephalography (EEG) in a mixed-list design. Participants encoded each list item, either aloud or silently during the study phase and made remember/know/new judgments in the test phase, while EEG data were recorded. …”
Get full text

Increase in beta frequency phase synchronization and power after a session of high frequency repetitive transcranial magnetic stimulation to the primary motor cortex by Enrico De Martino, Adenauer Girardi Casali, Bruno Andry Nascimento Couto, Thomas Graven-Nielsen, Daniel Ciampi de Andrade

“…Here, we stimulated four cortical targets used for rTMS (M1; dorsolateral-prefrontal cortex, DLPFC; anterior cingulate cortex, ACC; posterosuperior insula, PSI) with TMS coupled with high-density electroencephalography (TMS-EEG) to measure cortical excitability and oscillatory dynamics before and after active- and sham-M1-rTMS. …”
Get full text