Wearable EEG-Based Brain–Computer Interface for Stress Monitoring
Brian Premchand; Liyuan Liang; Kok Soon Phua; Zhuo Zhang; Chuanchu Wang; Guo Ling; Jennifer Ang; Juliana Koh · 2024 · NeuroSci
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vig
Abstract by Brian Premchand; Liyuan Liang; Kok Soon Phua; Zhuo Zhang; Chuanchu Wang; Guo Ling; Jennifer Ang; Juliana Koh, NeuroSci (2024) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Reproducible brain-wide association studies require thousands of individuals
Negative / Null Result ReportDaily Left Prefrontal Transcranial Magnetic Stimulation Therapy for Major Depressive Disorder
Negative / Null Result ReportEarly Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years
Negative / Null Result ReportObjective evaluation of anosmia and ageusia in COVID‐19 patients: Single‐center experience on 72 cases
Negative / Null Result ReportPolarity and timing-dependent effects of transcranial direct current stimulation in explicit motor learning
Negative / Null Result ReportThe feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.3390/neurosci5040031