Sheehanpeele0489

This paper evaluated the pupillary light reflex of glaucomatous eyes in the presence of constant lighting via light-induced pupillometry using sample entropy. The study used 20 patients and 15 controls, applied three different light intensities to their eyes, and recorded the behavior of the pupil. This study has validated that there is a difference in the entropy of pupillary data in glaucoma and healthy eyes. We concluded that entropy analysis is an excellent method to differentiate glaucoma eyes with the control through light-induced pupillometry. Hence, pupillometry has potential clinical applications in glaucoma investigation.The aim of this study was to evaluate individual level of natural variability of electroencephalogram (EEG) based markers. Three linear alpha power variability, spectral asymmetry index, relative gamma power and three nonlinear methods Higuchi's fractal dimension, detrended fluctuation analysis, and Lempel-Ziv complexity were selected. The markers were evaluated over 15 sessions acquired in 14 months. The results indicate that individual natural variability for five of the selected markers is lower compared to differences between healthy and depressed groups of subjects in our previous studies. The results of the current study suggest that EEG based markers can be applied for evaluation of disturbances in brain activity at individual level.Clinical Relevance-The indicated stability in the current study of widely used EEG-based markers at individual level suggests a promising opportunity to apply EEG as a novel method in diagnoses of brain mental disorders in clinical practice.A brain-computer interface (BCI) potentially enables a severely disabled person to communicate using brain signals. Automatic detection of error-related potentials (ErrPs) in electroencephalograph (EEG) could improve BCI performance by allowing to correct the erroneous action made by the machine. However, the current low accuracy in detecting ErrPs, particularly in some users, can reduce its potential benefits. The paper addresses this problem by proposing a novel relative peak feature (RPF) selection method to improve performance and accuracy for recognising an ErrP in the EEG. Using data collected from 29 participants with a mean age of 24.14 years the relative peak features yielded an average across all classifiers of 81.63% accuracy in detecting the erroneous events and an average 78.87 % accuracy in detecting the correct events, using KNN, SVM and LDA classifiers. In comparison to the temporal feature selection, there was a gain in performance in all classifiers of 17.85% for error accuracy and a reduction of -6.16% for correct accuracy Specifically; our proposed RPF used significantly reduced the number of features by 91.7% when compared with the state of the art temporal features.In the future, this work will improve the human-robot interaction by improving the accuracy of detecting errors that enable the BCI to correct any mistakes.We propose a method with attention-based recurrent neural networks (ARNN) for detecting the semantic incongruities in spoken sentences using single-trial electroencephalogram (EEG) signals. 19 participants listened to sentences, some of which included semantically anomalous words. We recorded their EEG signals while they listened. Although previous detection approaches used a word's explicit onset, we used the EEG signals of the whole regions of each sentence, which made it possible to classify the correctness of the sentences without the onset information of the anomalous words. ARNN achieved 63.5% classification accuracy with a statistical significance above the chance level and also above the performances which includes onset information (50.9%). Our results also demonstrated that the attention weights of the model showed that the predictions depended on the feature vectors that are temporally close to the onsets of the anomalous words.Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of penand-paper tests. These tests can be unreliable due to high variablility in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this paper, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our overall goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Filgotinib cell line Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect.Clinical relevance-The proposed EEG-based BCI can be used to detect neglect in stroke patients with high accuracy, specificity and sensitivity. Further research will additionally allow for an estimation of a patient's field of view (FOV) for more detailed assessment of neglect.The cross-subject variability, or individuality, of electroencephalography (EEG) signals often has been an obstacle to extracting target-related information from EEG signals for classification of subjects' perceptual states. In this paper, we propose a deep learning-based EEG classification approach, which learns feature space mapping and performs individuality detachment to reduce subject-related information from EEG signals and maximize classification performance. Our experiment on EEG-based video classification shows that our method significantly improves the classification accuracy.In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD). AD is preceded by Mild Cognitive Impairment (MCI), typically refers to early-stage AD disease. The purpose of this study is to classify MCI patients from the multi-domain features of their electroencephalography (EEG). Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls. Then, principal component analysis (PCA) was used to perform feature selection. After that, we compared the performance between SVM and KNN on our EEG dataset. The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features. Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.