November 17, 2022 Dataset Restricted Access

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

DCAT Export

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        <foaf:name>Garg, Nikhil</foaf:name>
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        <foaf:name>Garg, Rohit</foaf:name>
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        <foaf:name>Anand, Apoorv</foaf:name>
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    <dct:title>OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2022</dct:issued>
    <dcat:keyword>Electroencephalography (EEG)</dcat:keyword>
    <dcat:keyword>Brain Computer Interface (BCI)</dcat:keyword>
    <dcat:keyword>Machine learning</dcat:keyword>
    <dcat:keyword>Valence</dcat:keyword>
    <dcat:keyword>Arousal</dcat:keyword>
    <dcat:keyword>Emotion</dcat:keyword>
    <dcat:keyword>Feature engineering</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2022-11-17</dct:issued>
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    <dct:description>&lt;p&gt;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.&amp;nbsp;&lt;/p&gt; &lt;p&gt;Code: &lt;a href="https://github.com/rohitgarg025/Decoding_EEG"&gt;https://github.com/rohitgarg025/Decoding_EEG&lt;/a&gt;&lt;/p&gt;</dct:description>
    <dct:description>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</dct:description>
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