An EEG Dataset for Brainwave Recording During Emotion Elicitation via Video Clips

Published: 24 September 2024| Version 1 | DOI: 10.17632/58rydc6vwc.1
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Description

An EEG dataset for Recording Brain Electroencephalography Signals Based on Emotion Elicitation Video Clip with voice or without voice. unicorn hybrid black EEG device (8 channel) is used to record the data. of 30 participants (20 male and 10 female). In a research on emotion recognition, 30 participants—10 females and 20 males—were shown four different video clips that were intended to elicit different emotions: fear, sorrow, happiness, and neutrality. EEG signals were continuously captured for the whole two minutes of each movie. This recorded each subject's EEG activity for two minutes throughout each mood, showing how their brains responded to the emotional cues. Apart from regular sound-assisted movies, the research also featured four specially made emotion-transmitting videos for those who are deaf. This made it possible to analyze brain activity in great detail in response. Data sets consist three folder namely EEG signals (RAW Data) , Participants information and Video clips.

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Steps to reproduce

The Unicorn Hybrid Black EEG is used to create own data sets. it is a portable electroencephalography system with eight channels that is intended for real-time, high-quality brainwave recording. With its wireless capabilities and small size, it provides versatility for consumer, clinical, and research applications. Because the gadget has dry electrodes instead of conductive gels, setup is simple and quick. It is perfect for research involving brain-computer interfaces (BCIs), cognitive monitoring, and neurofeedback because it offers reliable EEG signal capture with high resolution and low noise. Because to its intuitive design and interoperability with a wide range of software programs, researchers may run experiments and evaluate brain activity with efficiency.

Institutions

Sharda University

Categories

Medical Physics, Medical Biotechnology, Neurocomputing, Medical Biology

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