In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature ...
In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature ...
Mar 29, 2021 · In, the study proposed to recognize human emotions by combining six statistical parameters of EEG signals from the time domain as EEG features.
Despite this highly noisy dataset, we are trying to achieve reasonable detection accuracy for the four emotions, anger, fear, joy and sad , and low false ...
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Oct 3, 2024 · We provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface ...
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In this thesis, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature ...
This paper proposes an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique and uses a feature selection ...
The results demonstrate that data augmentation can significantly improve the accuracy of emotion prediction for individual subjects, with the noise method ...
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The EEG data in the process of collecting may include noise which must be pre-processed (described in the next section) and then input into the model. The ...
In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature ...