10.3389/fnhum.2022.1051463
https://zenodo.org/records/7332684
oai:zenodo.org:7332684
Garg, Nikhil
Nikhil
Garg
UMR8520 Institut d'électronique, de microélectronique et de nanotechnologie (IEMN), France
Garg, Rohit
Rohit
Garg
Birla Institute of Technology and Science, India
Anand, Apoorv
Apoorv
Anand
Birla Institute of Technology and Science, India
Baths, Veeky
Veeky
Baths
Birla Institute of Technology and Science, India
OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset
Zenodo
2022
Electroencephalography (EEG)
Brain Computer Interface (BCI)
Machine learning
Valence
Arousal
Emotion
Feature engineering
2022-11-17
1
Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The collected dataset and pipeline are made open source.
Code: https://github.com/rohitgarg025/Decoding_EEG
Please cite as:
N. Garg, R. Garg, A. Anand, V. Baths, "Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset," Frontiers in Human Neuroscience, Nov. 2022. Doi: 10.3389/fnhum.2022.1051463