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Stroop Test

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Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

The international journal of neuropsychopharmacology 25964505
BACKGROUND: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographi...
Age Factors Attention Deficit Disorder with Hyperactivity Body Weight Brain Central Nervous System Stimulants Child Cotinine Drug Therapy, Computer-Assisted Female Humans Lead Magnetic Resonance Imaging Male Methylphenidate Norepinephrine Plasma Membrane Transport Proteins Polymorphism, Genetic Prognosis Receptors, Adrenergic, alpha-2 Rest Stroop Test Support Vector Machine Treatment Outcome
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Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 29096213
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted ...
Algorithms Attention Deficit Disorder with Hyperactivity Child Electroencephalography Evoked Potentials Humans Machine Learning Male Stroop Test Time Factors
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Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.

Journal of neural engineering 30524056
OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In ...
Algorithms Attention Brain-Computer Interfaces Color Perception Deep Learning Electroencephalography Humans Machine Learning Neural Networks, Computer Stroop Test Transfer, Psychology
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Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress.

Nutrients 31877912
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects ( = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine in...
Adult Autonomic Nervous System Cognition Dehydration Galvanic Skin Response Heart Rate Humans Machine Learning Male Sensitivity and Specificity Stress, Psychological Stroop Test Support Vector Machine Young Adult
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Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BMC psychiatry 39103819
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relati...
Adult Attention Deficit Disorder with Hyperactivity Case-Control Studies Female Galvanic Skin Response Heart Rate Humans Machine Learning Male Sensitivity and Specificity Stroop Test Support Vector Machine Wearable Electronic Devices Young Adult
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