Wearable electroencephalography and multi-modal mental state classification: A systematic literature review

BACKGROUND

Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.

METHOD

Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.

RESULTS

Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.

CONCLUSIONS

Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.

Exploration of effective electroencephalography features for the recognition of different valence emotions

Recent studies have shown that the recognition and monitoring of different valence emotions can effectively avoid the occurrence of human errors due to the decline in cognitive ability. The quality of features directly affects emotion recognition results, so this manuscript explores the effective electroencephalography (EEG) features for the recognition of different valence emotions. First, 110 EEG features were extracted from the time domain, frequency domain, time-frequency domain, spatial domain, and brain network, including all the current mainly used features. Then, the classification performance, computing time, and important electrodes of each feature were systematically compared and analyzed on the self-built dataset involving 40 subjects and the public dataset DEAP. The experimental results show that the first-order difference, second-order difference, high-frequency power, and high-frequency differential entropy features perform better in the recognition of different valence emotions. Also, the time-domain features, especially the first-order difference features and second-order difference features, have less computing time, so they are suitable for real-time emotion recognition applications. Besides, the features extracted from the frontal, temporal, and occipital lobes are more effective than others for the recognition of different valence emotions. Especially, when the number of electrodes is reduced by 3/4, the classification accuracy of using features from 16 electrodes located in these brain regions is 91.8%, which is only about 2% lower than that of using all electrodes. The study results can provide an important reference for feature extraction and selection in emotion recognition based on EEG.

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features.

Reading psychology and news communication strategies for affective computing

Reading psychology is an important basis for formulating news strategies. The purpose of this paper is to study how to analyze and study the psychological mechanism of reading and news communication strategies based on affective computing. It described the conditional random field. This paper put forward the problem of affective computing, which is based on affective computing technology. Then it expounded the concept of conditional random fields and related algorithms, and designed and analyzed cases of news communication strategies. Through the period from April 1, 2022 to June 1, 2022, the 300 short videos of news and information with the highest playback volume respectively released by Z software were used as research samples, and a multi-angle analysis was carried out. Among the 300 selected news information videos, 252 videos are original content, accounting for 84.00%, and the other short videos are from the self-media of the platform. At present, it is necessary to continuously develop intelligent products that meet the needs of users at different stages, and carry out personalized design in strict accordance with the living habits and natural conditions of different groups of people. Then continuously improving the experience of different groups of people using new media products, so that their reading interest can be more effectively stimulated.

Construction of interactive health education model for adolescents based on affective computing

At present, people mainly focus on health education for adolescents. The health education of adolescents is related to future of adolescents. In youth, their emotions are easily influenced. Therefore, this manuscript constructs an interactive health education model for adolescents through affective computing. Researchers in various countries have done a lot of research on human–computer interaction, and affective computing is one of the research hotspots. This manuscript aims to study the use of affective computing to construct an interactive health education model for adolescents. It proposed an interactive emotional algorithm based on emotional computing and focuses on the ICABoost algorithm. The experimental results of this paper show that the surveyed junior high school students are divided into three grades: the first, second, and third grades. Among them, 11, 11, and 13 were mentally healthy, with a total percentage of only 18.5%; 16, 14, and 16 were moderately severe in health education, accounting for 24.3%. The percentage of severe cases was 29.6%. It can be seen that, through the investigation of this manuscript, it can be seen that today’s youth health education should be paid attention to. Only by constructing a corresponding interactive health education model for young people can we promote the comprehensive and healthy development of young people.