相关论文: Locked in Syndrome Machine Learning Classification…
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi Fractal Dimension (HFD) and…
Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. PD patients report experiencing tremor, rigidity, visual impairment, bradykinesia, and several…
Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to…
Objective dyslexia diagnosis is not a straighforward task since it is traditionally performed by means of the intepretation of different behavioural tests. Moreover, these tests are only applicable to readers. This way, early diagnosis…
Semantically meaningful sentence embeddings are important for numerous tasks in natural language processing. To obtain such embeddings, recent studies explored the idea of utilizing synthetically generated data from pretrained language…
Application of electroencephalography (EEG) in educational research has grown substantially, yet a comprehensive integration of methodological frameworks, educational constructs, computational methods, and research gaps remains limited.…
This paper describes a multi-modal approach for the automatic detection of Alzheimer's disease proposed in the context of the INESC-ID Human Language Technology Laboratory participation in the ADReSS 2020 challenge. Our classification…
Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological…
Epilepsy is a neurological condition such that it affects the brain and the nervous system. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of…
The use of Automatic speech recognition (ASR) interfaces have become increasingly popular in daily life for use in interaction and control of electronic devices. The interfaces currently being used are not feasible for a variety of users…
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in…
The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying…
In recent years, machine learning has become an increasingly powerful tool for supporting seizure detection and monitoring in epilepsy care. Traditional approaches focus on identifying seizures only after they begin, which limits the…
Preclinical sleep research remains constrained by labor intensive, manual vigilance state classification and inter rater variability, limiting throughput and reproducibility. This study presents an automated framework developed by Team…
The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the brain cortex has been seen as a prevalent approach to…
Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are…
Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring…
Processing and analyzing of massive clinical data are resource intensive and time consuming with traditional analytic tools. Electroencephalogram (EEG) is one of the major technologies in detecting and diagnosing various brain disorders,…
According to the World Health Organization (WHO), cancer is the second leading cause of death globally. Scientific research on different types of cancers grows at an ever-increasing rate, publishing large volumes of research articles every…
Electroencephalography produces high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest. We address this challenge by applying the framework of variational…