Li Jia Wen, Lin Di, Che Yan, Lv Ju Jian, Chen Rong Jun, Wang Lei Jun, Zeng Xian Xian, Ren Jin Chang, Zhao Hui Min, Lu Xu
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.
Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, China.
Front Neurosci. 2023 Jul 20;17:1221512. doi: 10.3389/fnins.2023.1221512. eCollection 2023.
Efficiently recognizing emotions is a critical pursuit in brain-computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.
These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.
The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83-92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.
Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.
在脑机接口（BCI）中，高效识别情绪是一项至关重要的工作，因为它在智能医疗服务中有许多应用。在这项工作中，提出了一种受生物信息学中遗传密码启发的创新方法，该方法利用由δ、θ、α、β或γ组成的脑节律编码特征进行基于脑电图（EEG）的情绪识别。
这些特征首先从测序技术中提取。在使用四种传统机器学习分类器对其进行评估后，确定了在每种情绪情况下产生最高准确率的最佳通道特异性特征，从而通过最少的数据实现情绪识别。通过这样做，可以显著降低情绪识别的复杂性，使其在实际硬件设置中更易于实现。
在DEAP和MAHNOB数据集上取得的最佳分类准确率在83%-92%之间，在SEED数据集上为78%。考虑到所使用的数据最少，实验结果令人印象深刻。对最佳特征的进一步研究表明，它们的代表性通道主要位于额叶区域，并且相关的节律特征具有多种类型。此外，还发现了个体差异，因为最佳特征因受试者而异。
与之前的研究相比，这项工作为便携式设备的设计提供了见解，因为仅一个电极就足以产生令人满意的性能。因此，它将推进对脑节律的理解，这为包括情绪识别在内的各种BCI应用中的EEG信号分类提供了一种创新解决方案。
