Brain computer interface based emotion recognition with error analysis and challenges: an interdisciplinary review
Abstract Emotion recognition is defined as identifying a person’s emotional condition through information such as facial expressions and behavior. Modern advances in brain–computer interfaces (BCIs) have shown that they are effective in converting electrical signals from the brain into cognitive pro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-06692-0 |
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| Summary: | Abstract Emotion recognition is defined as identifying a person’s emotional condition through information such as facial expressions and behavior. Modern advances in brain–computer interfaces (BCIs) have shown that they are effective in converting electrical signals from the brain into cognitive processes. Certain regions of the brain and central nervous system have shown that electroencephalogram (EEG) can provide a clearer picture of a person’s emotional state than nonverbal indicators. Therefore, emotion recognition through BCIs holds significant promise for various domains, including affective computing, healthcare, and human–computer interaction, with numerous potential applications. Creating precise and reliable emotion recognition systems based on BCIs presents significant challenges due to the multitude of potential error sources that can affect their effectiveness. In this review, we address the main sources of errors in BCI-based emotion recognition from an interdisciplinary perspective, integrating knowledge from neuroscience, signal processing, machine learning, and psychology. We analyze how various factors, such as brain signal variability, EEG noise and artifacts, individual differences in emotional responses, feature extraction methods, and classification algorithms, can introduce errors into the system. Additionally, we provide an overview of the challenges involved in implementing BCI-based emotion recognition systems in real-world scenarios, where environmental noise and user adaptation are critical issues. The review emphasizes the need to understand and address these error sources to develop robust BCI-based emotion recognition systems that can provide accurate and reliable emotion detection in various contexts. |
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| ISSN: | 3004-9261 |