Year: 2025
Type: conference-paper
Abstract: Achieving a dataset of eye detection comprises a critical task in computer vision and image processing. The primary goal of this dataset is to accurately locate and identify the position of eyes in image or video frames. This process can firstly detect the face region and then focus on the eye regions. In this study, 14,980 examples of physiological signal recordings, most likely from EEG or similar sensors, were included in this dataset, which was created for the analysis of neural or sensor-based movement. The constant signals from specific sensor channels are represented by 14 numerical features (AF3, F7, F3, O1, O2, P7, P8, T8, FC5, FC6, etc.). These characteristics record complex changes in signal designs over time, which could suggest shifts in sensor or neuronal activity. Also, the dataset involves a binary target variable called eye detection, and this shows if an eye-related event—such as turning or an open/closed state—is identified during an individual case. The basic label of this dataset is eye detection in human beings, which has instances of (0,1). The eye detection dataset has 14 features and 14,980 instances that can be utilized for training a model.
Authors: Masroor Ahmad, Tahir Muhammad Ali, Nunik Destria Arianti
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4415172708