Khajeh Hosseini Mohammad Saleh, Pourmir Firoozabadi Mohammad, Badie Kambiz, Azad Fallah Parviz
Department of Biomedical Engineering, Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Medical Physics, Faculty of Medicine, Tarbiat Modares University, Tehran, Iran.
Basic Clin Neurosci. 2023 Sep-Oct;14(5):687-700. doi: 10.32598/bcn.2022.3830.2. Epub 2023 Sep 1.
The study explores the use of Electroencephalograph (EEG) signals as a means to uncover various states of the human brain, with a specific focus on emotion classification. Despite the potential of EEG signals in this domain, existing methods face challenges. Features extracted from EEG signals may not accurately represent an individual's emotional patterns due to interference from time-varying factors and noise. Additionally, higher-level cognitive factors, such as personality, mood, and past experiences, further complicate emotion recognition. The dynamic nature of EEG data in terms of time series introduces variability in feature distribution and interclass discrimination across different time stages.
To address these challenges, the paper proposes a novel adaptive ensemble classification method. The study introduces a new method for providing emotional stimuli, categorizing them into three groups (sadness, neutral, and happiness) based on their valence-arousal (VA) scores. The experiment involved 60 participants aged 19-30 years, and the proposed method aimed to mitigate the limitations associated with conventional classifiers.
The results demonstrate a significant improvement in the performance of emotion classifiers compared to conventional methods. The classification accuracy achieved by the proposed adaptive ensemble classification method is reported at 87.96%. This suggests a promising advancement in the ability to accurately classify emotions using EEG signals, overcoming the limitations outlined in the introduction.
In conclusion, the paper introduces an innovative approach to emotion classification based on EEG signals, addressing key challenges associated with existing methods. By employing a new adaptive ensemble classification method and refining the process of providing emotional stimuli, the study achieves a noteworthy improvement in classification accuracy. This advancement is crucial for enhancing our understanding of the complexities of emotion recognition through EEG signals, paving the way for more effective applications in fields such as neuroinformatics and affective computing.
本研究探讨了利用脑电图（EEG）信号来揭示人类大脑的各种状态，特别关注情绪分类。尽管EEG信号在这一领域具有潜力，但现有方法仍面临挑战。由于时变因素和噪声的干扰，从EEG信号中提取的特征可能无法准确代表个体的情绪模式。此外，诸如个性、情绪和过去经历等更高层次的认知因素，进一步使情绪识别变得复杂。EEG数据在时间序列方面的动态性质，在不同时间阶段引入了特征分布和类间区分的变异性。
为应对这些挑战，本文提出了一种新颖的自适应集成分类方法。该研究引入了一种提供情绪刺激的新方法，根据效价-唤醒（VA）分数将其分为三组（悲伤、中性和快乐）。实验涉及60名年龄在19至30岁之间的参与者，所提出的方法旨在减轻与传统分类器相关的局限性。
结果表明，与传统方法相比，情绪分类器的性能有了显著提高。所提出的自适应集成分类方法实现的分类准确率报告为87.96%。这表明在利用EEG信号准确分类情绪的能力方面有了有前景的进展，克服了引言中概述的局限性。
总之，本文介绍了一种基于EEG信号的情绪分类创新方法，解决了与现有方法相关的关键挑战。通过采用新的自适应集成分类方法并完善提供情绪刺激的过程，该研究在分类准确率方面取得了显著提高。这一进展对于增强我们通过EEG信号理解情绪识别复杂性至关重要，为神经信息学和情感计算等领域的更有效应用铺平了道路。
