Design Decisions for Wearable EEG to Detect Motor Imagery Movements DOI Creative Commons
Ana Carretero, Álvaro Araújo

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4763 - 4763

Published: July 23, 2024

The objective of this study was to make informed decisions regarding the design wearable electroencephalography (wearable EEG) for detection motor imagery movements based on testing critical features development EEG. Three datasets were utilized determine optimal acquisition frequency. brain zones implicated in movement analyzed, with aim improving wearable-EEG comfort and portability. Two algorithms different configurations implemented. output classified using a tool various classifiers. results categorized into three groups discern differences between general hand no movement; specific other (between five finger movement). Testing conducted sampling frequencies, trials, number electrodes, algorithms, their parameters. preferred algorithm determined be FastICACorr 20 components. frequency is 1 kHz avoid adding excessive noise ensure efficient handling. Twenty trials are deemed sufficient training, electrodes will range from one three, depending EEG's ability handle parameters good performance.

Language: Английский

Wearables in Chronomedicine and Interpretation of Circadian Health DOI Creative Commons
Denis Gubin,

Dietmar Weinert,

Oliver Stefani

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 327 - 327

Published: Jan. 30, 2025

Wearable devices have gained increasing attention for use in multifunctional applications related to health monitoring, particularly research of the circadian rhythms cognitive functions and metabolic processes. In this comprehensive review, we encompass how wearables can be used study disease. We highlight importance these as markers well-being potential predictors outcomes. focus on wearable technologies sleep research, medicine, chronomedicine beyond domain emphasize actigraphy a validated tool monitoring sleep, activity, light exposure. discuss various mathematical methods currently analyze actigraphic data, such parametric non-parametric approaches, linear, non-linear, neural network-based applied quantify non-circadian variability. also introduce novel actigraphy-derived markers, which personalized proxies status, assisting discriminating between disease, offering insights into neurobehavioral status. lifestyle factors physical activity exposure modulate brain health. establishing reference standards measures further refine data interpretation improve clinical The review calls existing tools methods, deepen our understanding health, develop healthcare strategies.

Language: Английский

Citations

2
The Potential of Wearable Sensors for Detecting Cognitive Rumination: A Scoping Review DOI Creative Commons
Vitica Arnold, Sean D. Young

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 654 - 654

Published: Jan. 23, 2025

Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators associated with rumination. This scoping review investigates current state research on using technology to detect cognitive Specifically, we examine sensors devices used, biomarkers measured, standard rumination comparative validity specific identifying was performed according Preferred Reporting Items Systematic reviews Meta-Analyses (PRISMA) guidelines IEEE, Scopus, PubMed, PsycInfo databases. Studies that used measure rumination-related responses were included (n = 9); seven studies one biomarker, two biomarkers. Electrodermal Activity (EDA) capturing skin conductance activity emerged as both most prevalent sensor 5) comparatively valid biomarker detecting via devices. Other commonly investigated electrical brain measured Electroencephalogram (EEG) 2), Heart Rate Variability (HRV) Electrocardiogram (ECG) heart rate fitness monitors muscle response Electromyography (EMG) 1) movement an accelerometer 1). Empatica E4 Embrace 2 wrist-worn frequently 3). Rumination Response Scale (RRS), widely scale assessing Experimental induction protocols, often adapted from Nolen-Hoeksema Morrow’s 1993 paradigm, also used. In conclusion, findings suggest promise field is still developing, further needed validate explore impact individual traits contextual factors accuracy detection.

Language: Английский

Citations

1
Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification DOI Creative Commons

Sherzod Abdumalikov,

Jingeun Kim, Yourim Yoon

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10511 - 10511

Published: Nov. 14, 2024

Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. In this study, the of emotional states using electroencephalography (EEG) data were investigated. Specifically, efficacy combination various feature selection methods and hyperparameter tuning machine learning algorithms for accurate robust emotion recognition was studied. The following explored: filter (SelectKBest analysis variance (ANOVA) F-test), embedded (least absolute shrinkage operator (LASSO) tuned Bayesian optimization (BO)), wrapper (genetic algorithm (GA)) methods. We also executed BO. performance each method assessed. Two different EEG datasets, DEAP Dataset, containing 2548 160 features, respectively, evaluated random forest (RF), logistic regression, XGBoost, support vector (SVM). For both experimented three consistently improved accuracy models. dataset, RF LASSO achieved best result among all increasing 98.78% 99.39%. dataset experiment, XGBoost GA showed result, by 1.59% 2.84% valence arousal. show that these results are superior those previous other literature.

Language: Английский

Citations

4
Use of Complementary and Alternative Methods of Pain Management DOI

Erika Haase,

C. B. MOORE

Nursing Clinics of North America, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Brain Network Alterations in Fragile X Syndrome DOI
Flavia Venetucci Gouveia,

Jürgen Gernmann,

George M. Ibrahim

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 106101 - 106101

Published: March 1, 2025

Language: Английский

Citations

0
Application of imaging photoplethysmography in surgical procedures: A review article DOI Creative Commons
Xuan Qiu,

L. Ye,

Xu-Peng Liu

et al.

Asian Journal of Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Motor Rehabilitation and Biofeedback DOI
K. Jayasankara Reddy

Published: Jan. 1, 2025

Language: Английский

Citations

0
Cognitive Training Programs DOI
K. Jayasankara Reddy

Published: Jan. 1, 2025

Language: Английский

Citations

0
Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy DOI Creative Commons

Erica Iammarino,

Ilaria Marcantoni, Agnese Sbrollini

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4679 - 4679

Published: July 18, 2024

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by varies with the location of electrodes, which can be obtained using standard multi-channel recorders. Cognitive assessed during working memory (WM) tasks, testing mental ability to process over short period time. WM could impaired in patients epilepsy. This study aims evaluate nine epilepsy, coming from public dataset Boran et al., verbal task and identify most electrodes this purpose. was evaluated computing 37 indexes based on ratio two or more rhythms their spectral power. Results show that involvement index trends follow changes elicited task, and, overall, appear pronounced frontal regions, as observed healthy subjects. Therefore, reflect status changes, regions seem ones focus when designing system, both physiological epileptic conditions.

Language: Английский

Citations

0
Design Decisions for Wearable EEG to Detect Motor Imagery Movements DOI Creative Commons
Ana Carretero, Álvaro Araújo

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 4763 - 4763

Published: July 23, 2024

The objective of this study was to make informed decisions regarding the design wearable electroencephalography (wearable EEG) for detection motor imagery movements based on testing critical features development EEG. Three datasets were utilized determine optimal acquisition frequency. brain zones implicated in movement analyzed, with aim improving wearable-EEG comfort and portability. Two algorithms different configurations implemented. output classified using a tool various classifiers. results categorized into three groups discern differences between general hand no movement; specific other (between five finger movement). Testing conducted sampling frequencies, trials, number electrodes, algorithms, their parameters. preferred algorithm determined be FastICACorr 20 components. frequency is 1 kHz avoid adding excessive noise ensure efficient handling. Twenty trials are deemed sufficient training, electrodes will range from one three, depending EEG's ability handle parameters good performance.

Language: Английский

Citations

0