Multidimensional Emotion Recognition Based on Semantic Analysis of Biomedical EEG Signal for Knowledge Discovery in Psychological Healthcare

Abstract

Electroencephalogram (EEG) as biomedical signal is widely applied in the medical field such as the detection of Alzheimer’s disease, Parkinson’s disease, etc. Moreover, by analyzing the EEG-based emotions, the mental status of individual can be revealed for further analysis on the psychological causes of some diseases such as cancer, which is considered as a vital factor on the induction of certain diseases. Therefore, once the emotional status can be correctly analyzed based on EEG signal, more healthcare-oriented applications can be furtherly carried out. Currently, in order to achieve efficiency and accuracy, diverse amounts of EEG-based emotions recognition methods generally extract features by analyzing the overall characteristics of signal, along with optimization strategy of channel selection to minimize the information redundancy. Those methods have been proved their effectiveness, however, there still remains a big challenge when applied with single channel information for emotion recognition task. Therefore, in order to recognize multidimensional emotions based on single channel information, an emotion quantification analysis (EQA) method is proposed to objectively analyze the semantically similarity between emotions in valence-arousal domains, and a multidimensional emotion recognition (EMER) model is proposed on recognizing multidimensional emotions according to the partial fluctuation pattern (PFP) features based on single channel information, and result shows that even though semantically similar emotions are proved to have similar change patterns in EEG signals, each single channel of 4 frequency bands can efficiently recognize 20 different emotions with an average accuracy above 93% separately.

EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare

“…So, in our testing, in addition to using the well-known open datasets to discuss the recognition accuracy for emotions, we also used Muse 2, Emotiv insight, and Mindlink to gather EEG signals and test the emotional recognition accuracy. In our previous study, for different types of channel selections, the average recognition rate could reach 93.5% [31]. At the core of improving the accuracy of multiple types of emotion calculations is semantic analysis for classification.…”

“…There were 20 typical core emotions derived by calculating the similarity and angle [30]. Moreover, we tested the real EEG signals based on these 20 emotion types, which exactly appeared with significant arousal rates [31]. After semantic distance calculating, Figure 2 shows these 20 kinds of emotions' distributions in the valence arousal model.…”

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding

“…The abundant data in the dataset can facilitate the utilization of modern data-driven methods from NLP in language related tasks, such as training large-scale models to learn the complex semantic patterns in neural signals, and aligning neural signals with natural languages in the representation space. For example, by using large-scale neural data to train deep learning models, these models can effectively learn the complex semantic representations of the brain under linguistic stimuli and generalize well across a wide 6/15 range of downstream tasks, such as semantic decoding 39 , text-based emotion recognition 40 Given that most existing EEG datasets primarily focus on English language materials, the ChineseEEG dataset can be especially useful for exploring both scientific problems and practical applications in the context of Chinese language, prompting cross-cultural research in related fields.…”

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

“…And Salankar et al (2021) first adapted EMD to decomposes the signals into several oscillatory IMF and then extracted features including area, mean, and central tendency measure of the elliptical region from second-order difference plots (SODP). In the same year, Wang et al (2021) proposed an emotion quantification analysis (EQA) method, which was conducted based on the emotional similarity quantification (ESQ) algorithm in which each emotion was mapped in the valence-arousal domains according to the emotional similarity matrixes.…”