Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1515/bmt-2018-0105 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument for assessing emotional well-being. Most current models neglect the significance of integrating viewpoints in comprehending mental health in favor of single-task learning. To offer a more thorough knowledge of mental health, in this study, we present an Opinion-Enhanced Hybrid BERT Model (Opinion-BERT), built to handle multi-task learning for simultaneous sentiment and status categorization.
View Article and Find Full Text PDFNPJ Sci Learn
January 2025
Department of Psychology, The Education University of Hong Kong, Hong Kong, China.
Statistical learning is a core ability for individuals in extracting and integrating regularities and patterns from linguistic input. Yet, the developmental trajectory of visual statistical learning has not been fully examined in the orthographic learning domain. Employing an artificial orthographic learning task, we manipulated three levels of positional consistency of radicals, i.
View Article and Find Full Text PDFEpilepsia
January 2025
Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Mental health (MH) comorbidities are prevalent among people with epilepsy (PWE), but many experience challenges accessing care. To address this, suggestions have been made to integrate MH care into epilepsy care settings, yet the current approaches, benefits, and implementation determinants to MH care integration are unclear. This review aims to synthesize existing integrated MH care models for PWE to inform the development and planning of future initiatives.
View Article and Find Full Text PDFMental Health Sci
September 2024
Department of Psychiatry, UC San Diego, La Jolla, CA, USA.
Background: The influence of alcohol use on later neurocognitive functioning is well researched, yet few studies have investigated whether neurocognition post-drinking initiation in adolescence predicts changes in later alcohol use.
Objective: Investigate neurocognitive task performance during maximum alcohol use in late adolescence as predictors of drinking behaviors 3-7 years later.
Methods: Analyses () were conducted on a longitudinal dataset involving adolescents (12-13 years-old) who were followed for 16 years.
MethodsX
June 2025
Universidad de Virginia, Charlottesville, United States.
The research aims to evaluate the effect of a robotics-based computational thinking program on executive functions and visuospatial skills in preschool children. Additionally, the study will explore the relationship between these three variables and early experiences with toys. The study will be a cluster-randomized controlled trial with pre- and post-intervention measures.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!