Synergy through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening

Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer's disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

A low-cost, wireless, 4-channel EEG measurement system used in virtual reality environments

The combination of Virtual Reality (VR) technology and Electroencephalography (EEG) measurements has shown tremendous potential in the fields of psychology and neuroscience research. However, the majority of EEG measurement devices currently available are expensive, bulky, uncomfortable to wear, and difficult to integrate with VR headsets. These limitations have hindered the development of related research fields. This study describes a low-cost (60.07 USD), small-sized, wireless, high-precision, low-power consumption 4-channel EEG measurement system (NeuroVista) for frontal area EEG measurements, which can be used with a VR headset, enabling EEG measurements in VR environments. The system has an input-referred noise of less than 0.9480 μ V r m s , a common mode rejection ratio of over 96 dB, a measurement resolution of less than 0.1 μ V , a bandwidth of 0.5 ∼ 45 Hz, and works at a sampling rate of 250 Hz. It also supports metal dry electrodes and includes a built-in analog bandpass filter, right-leg drive circuit, and built-in digital lowpass filter and notch filter, which can reduce noise during measurement. Researchers can reconstruct the electrode system to measure regions of interest according to their needs.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus' EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Introduction

Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.

Methods

We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.

Results

The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.

Discussion

It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.