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- W3142958216 endingPage "4401" @default.
- W3142958216 startingPage "4385" @default.
- W3142958216 abstract "Emotion recognition based on electroencephalography (EEG) signal features is now one of the booming big data research areas. As the number of commercial EEG devices in the current market increases, there is a need to understand current trends and provide researchers and young practitioners with insights into future investigations of emotion recognition systems. This paper aims to evaluate popular consumer-grade EEG devices’ status and review relevant studies that examined the reliability of these low-cost devices for emotion recognition over the last five years. Additionally, a comparison with research-grade devices is conducted. This paper also highlights EEG-based emotion recognition research’s key areas, including different feature extraction capabilities, characteristics, and machine learning algorithms. Finally, the main challenges for building an EEG-based emotion recognition system, focusing on the data collection process with commercial EEG devices and machine learning algorithms’ performance, are presented." @default.
- W3142958216 created "2021-04-13" @default.
- W3142958216 creator A5045719777 @default.
- W3142958216 creator A5083214398 @default.
- W3142958216 creator A5087269137 @default.
- W3142958216 date "2022-07-01" @default.
- W3142958216 modified "2023-09-30" @default.
- W3142958216 title "EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques" @default.
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