Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

He, Congying; Chen, Yu-Yi; Phang, Chun-Ren; Stevenson, Cory; Chen, I‐Ping; Jung, Tzyy-Ping; Ko, Li-Wei

doi:10.1109/jbhi.2023.3239053

Cited by 28 publications

(11 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some wearables can be expensive, making them elusive to a large proportion of society, where such financial constraints potentially deprive many of benefiting from the technologies ability to support a healthy life (He et al, 2023 ). For democratization of wearables, it's pivotal for manufacturers to innovate.…”

Section: Developmental Challengesmentioning

confidence: 99%

Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences

Wall,

Celik,

Hetherington

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

Section: Developmental Challengesmentioning

confidence: 99%

Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences

Wall,

Celik,

Hetherington

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

“…EEG finds applications in various domains, including neuroscience, diagnosing neurological disorders, sleep research, and cognitive studies. It has emerged as a potent tool for comprehending brain activity and has significantly contributed to advancing our knowledge of brain functions and dysfunctions [43]. However, multiple challenges and complications have also been reported by the researchers and participants of these studies.…”

Section: Eegmentioning

confidence: 99%

The Use of Wearable Technology in Providing Assistive Solutions for Mental Well-Being

Alhejaili,

Alomainy

2023

Sensors

View full text Add to dashboard Cite

The main goal of this manuscript is to provide an extensive literature review and analysis of certain biomarkers, which are frequently used to identify stress, anxiety, and other emotions, leading to potential solutions for the monitoring of mental wellness using wearable technologies. It is possible to see the impacts of several biomarkers in detecting stress levels and their effectiveness with an investigation into the literature on this subject. Biofeedback training has demonstrated some psychological effects, such as a reduction in anxiety and self-control enhancement. This survey demonstrates backed up by evidence that wearable devices are assistive in providing health and mental wellness solutions. Because physical activity tracing would reduce the stress stressors, which affect the subject’s body, therefore, it would also affect the mental activity and would lead to a reduction in cognitive mental load.

show abstract

“…In recent years, there has been a substantial increase in the adoption of non-invasive devices for measuring brain activity, such as electroencephalography (EEG) ( Minguillon et al, 2017 ; He et al, 2023 ). The non-invasiveness and high temporal resolution make it a convenient and essential tool for research and clinical diagnosis of neurological diseases ( Perez-Valero et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

View full text Add to dashboard Cite

Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

show abstract

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Cited by 28 publications

References 73 publications

Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences

Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences

The Use of Wearable Technology in Providing Assistive Solutions for Mental Well-Being

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Contact Info

Product

Resources

About