Electroencephalogram (EEG) based emotional analysis has been employed in medical science, security and human-computer interaction with good success. In the recent past, deep learning-based approaches have significantly improved the classification accuracy when compared to classical signal processing and machine learning based frameworks. But most of them were subject-dependent studies which were not able to generalize on the subject-independent tasks due to the inter-subject variability present in EEG data. In this work, a novel deep learning framework capable of doing subject-independent emotion recognition is presented, consisting of two parts. First, an unsupervised Long Short-Term Memory (LSTM) with channel-attention autoencoder is proposed for getting a common latent vector subspace i.e., intrinsic variables present in the EEG data of each individual. Secondly, a convolutional neural network (CNN) with attention framework is presented for performing the task of subject-independent emotion recognition on the encoded lower dimensional latent space representations obtained from the proposed LSTM with channel-attention autoencoder. With the attention mechanism, the proposed approach could highlight the significant time-segments of the EEG signal, which contributes to the emotion under consideration as validated by the results. The proposed approach has been validated using publicly available datasets for EEG signals such as DEAP dataset, SEED dataset and CHB-MIT dataset. With the proposed methodology, average subject independent accuracies of 65.9%, 69.5% for valence and arousal classification in the DEAP dataset and 76.7% for positive-negative classification in SEED dataset are obtained. Further for the CHB-MIT dataset, average subject independent accuracies of 69.1%, 67.6%, 72.3% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification are obtained.
翻译:在医学、安全和人体计算机互动中,基于电动图(EEEG)的情感分析为67。在医学、安全和人体计算机互动中,基于情感分析取得了良好的成功。最近,深层次的基于学习的方法大大提高了与古典信号处理和机器学习框架相比的分类准确性。但大多数这些方法都是基于主题的研究,由于EEEG数据中存在的问题变异性,无法对独立的任务进行概括化。在这项工作中,介绍了一个能够进行主题独立情感识别的新深层次学习框架,由两部分组成。首先,一个具有频道注意力的远程内存(LSTM),有频道注意力的内存自动编码(LSTM),提出了用于获取共同潜伏矢量矢量矢量子子子子空间(即每个人 EEG数据中存在的内在变量。第二,一个带有关注框架的变动神经网络(CNN),用于完成对从提议的低度潜伏空间空间显示的识别任务,由频道自动解算获得的LSESTM。在关注机制下,拟议的方法可以突出显示EGEA平均数据(EG)中,用于对数据进行平均数据判值数据进行确认数据分析的数值分析,在EEEEEG数据中,在EG数据中,对数据中,对数据进行数据进行数据进行数据评算中,这是用于对数据分析的