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Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro.
PLoS One
January 2025
Instituto de Microelectrónica de Sevilla (IMSE-CNM), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Sevilla, Sevilla, Spain.
Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Approximately 30-40% of patients respond poorly to antiepileptic medications, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy.
View Article and Find Full Text PDFMeasuring self-similarity in empirical signals to understand musical beat perception.
Eur J Neurosci
January 2025
Institute of Neuroscience (IONS), UCLouvain, Brussels, Belgium.
Experiencing music often entails the perception of a periodic beat. Despite being a widespread phenomenon across cultures, the nature and neural underpinnings of beat perception remain largely unknown. In the last decade, there has been a growing interest in developing methods to probe these processes, particularly to measure the extent to which beat-related information is contained in behavioral and neural responses.
View Article and Find Full Text PDFPredicting the Irrelevant: Neural Effects of Distractor Predictability Depend on Load.
Eur J Neurosci
January 2025
Department of Psychology, University of Lübeck, Lübeck, Germany.
Distraction is ubiquitous in human environments. Distracting input is often predictable, but we do not understand when or how humans can exploit this predictability. Here, we ask whether predictable distractors are able to reduce uncertainty in updating the internal predictive model.
View Article and Find Full Text PDFA Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.
Entropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFEEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.
Brain Sci
January 2025
Department of Mechanical Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, India.
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment.
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