Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition

Dalilian, Fatemeh; Nembhard, David

doi:10.1080/10447318.2023.2208991

Cited by 4 publications

(1 citation statement)

References 114 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, among the multifaceted avenues within this realm, the utilization of electroencephalogram (EEG) signals for emotion detection emerges as a captivating prospect [7]. This technology unfurls a panorama of applications that span from precisiondriven control mechanisms in activities such as drone piloting and driving to the immersive landscapes of electronic gaming [8]. However, despite its immense potential, the domain of EEG-based emotion detection has encountered limited exploration.…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals

Ul Hassan,

Ali,

Abideen

et al. 2023

BioMedInformatics

View full text Add to dashboard Cite

Emotion detection assumes a pivotal role in the evaluation of adverse psychological attributes, such as stress, anxiety, and depression. This study undertakes an exploration into the prospective capacities of machine learning to prognosticate individual emotional states, with an innovative integration of electroencephalogram (EEG) signals as a novel informational foundation. By conducting a comprehensive comparative analysis of an array of machine learning methodologies upon the Kaggle Emotion Detection dataset, the research meticulously fine-tunes classifier parameters across various models, including, but not limited, to random forest, decision trees, logistic regression, support vector machines, nearest centroid, and naive Bayes classifiers. Post hyperparameter optimization, the logistic regression algorithm attains a peak accuracy rate of 97%, a proximate performance mirrored by the random forest model. Through an extensive regimen of EEG-based experimentation, the study underscores the profound potential of machine learning paradigms to significantly elevate the precision of emotion detection, thereby catalyzing advancements within the discipline. An ancillary implication resides in early discernment capabilities, rendering this investigation pertinent within the domain of mental health assessments.

show abstract

Section: Introductionmentioning

confidence: 99%