EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Automated EEG-Based Brainwave Analysis for the Detection of Postoperative Delirium Does Not Result in a Shorter Length of Stay in Geriatric Hip Fracture Patients: A Multicentre Randomized Controlled Trial.
J Clin Med
November 2024
Center for Geriatric Trauma, Department of Surgery, St. Antonius Hospital, 3543 AZ Utrecht, The Netherlands.
: Delirium in postoperative geriatric hip fracture patients is a serious and often preventable condition. If detected in time, it can be treated, but a delay in the diagnosis and initiation of treatment impairs outcomes. A novel approach to detect delirium is to use point-of-care electro-encephalogram (EEG) recording with automated analysis.
View Article and Find Full Text PDFEEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning.
Cogn Neurodyn
October 2024
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Schizophrenia (SZ) is a serious mental disorder that can mainly be distinguished by symptoms including delusions and hallucinations. This mental disorder makes difficult conditions for the person and her/his relatives. Electroencephalogram (EEG) signal is a sophisticated neuroimaging technique that helps neurologists to diagnose this mental disorder.
View Article and Find Full Text PDFA machine learning based depression screening framework using temporal domain features of the electroencephalography signals.
PLoS One
March 2024
Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan.
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively.
View Article and Find Full Text PDFThe high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings.
View Article and Find Full Text PDFImpacts of Cortical Regions on EEG-based Classification of Lexical Tones and Vowels in Spoken Speech.
Annu Int Conf IEEE Eng Med Biol Soc
July 2023
Speech impairment is one of the most serious problems for patients with communication disorders, e.g., stroke survivors.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!