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OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset

Garg, Nikhil; Garg, Rohit; Anand, Apoorv; Baths, Veeky

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. 


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
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