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• W4283366936 abstract "The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain-computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F[Formula: see text]-score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions." @default.
• W4283366936 created "2022-06-25" @default.
• W4283366936 creator A5009736033 @default.
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• W4283366936 date "2022-07-25" @default.
• W4283366936 modified "2023-10-14" @default.
• W4283366936 title "Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment" @default.
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• W4283366936 doi "https://doi.org/10.1142/s0129065722500411" @default.
• W4283366936 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35881017" @default.
• W4283366936 hasPublicationYear "2022" @default.
• W4283366936 type Work @default.
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