Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
ObjectiveTo investigate the video-electroencephalography (VEEG) characteristics of old patients with epilepsy (OPWE). MethodsBetween June 2013 and July 2014, 57 OPWE at an age over 60 years were assigned to research group and 65 adults between 16 and 60 years old with epilepsy were regarded as controls. All the subjects underwent VEEG for 24 hours covering awake state and sleep with hyperventilation test being applied. Chi square was used to compare occurrence rate of epileptic wave and abnormal response rate after hyperventilation between the two groups of patients. Additionally, ictal elcetroencephalograph (EEG) was analyzed. ResultsCommon features of waves on EEG for patients in both the two groups during the ictal period included widespread low amplitude fast wave (2 cases in the research group, 7.4%; 4 cases in the control group, 12.5%), focal low amplitude fast wave (5 cases in the research group, 18.5%; 6 cases in the control group, 18.8%), widespread spike or spike slowing complex (3 cases in the research group, 11.1%; 7 case in the control group, 21.8%), focal spike or spike slowing complex (5 cases in the research group, 14.9%; 8 cases in the control group, 25.0%), and focal rhythmic slow wave (6 cases in the research group, 18.5%; 6 cases in the control group, 18.8%). In the research group, there were two following cases:single abnormal background activity in 5 cases (18.5%), and neither abnormal background activity nor epileptic discharge in 1 case (3.7%). Ictal focal epileptic discharges were found in 16 cases in the research group and 8 in the control group (59.3% vs 25.0%), with statistical difference (P<0.05). Inter-ictal epilepsy discharges were found in 57 patients of the research group (awake, 15.8%; sleep, 52.6%), which was less than that in the control group (awake, 46.2%; sleep, 83.1%) with statistical difference (P<0.05), accompanied by focal slow wave (temporal intermittent rhythmic delta activity, TIRDA) in 9 cases. In natural sleep period, epilepsy discharge occurrences increased (65.3%). Abnormal response rate in the research group (14.0%) was lower than that in the control group (64.6%) with statistical difference (P<0.05). ConclusionEarly onset EEG of the old and the adult are similar except those with single abnormal background activity and those with neither abnormal background activity nor epileptic discharge. Focal onset on EEG is more frequently seen in OPWE than in APWE. In natural sleep, epileptic discharge increases among OPWE, and abnormal response during hyperventilation is less likely to happen in OPWE.
Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.
With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.
Objective To investigate the network reorganization and dynamic brain activity in visuospatial neglect (VSN) patients using resting-state electroencephalography (rEEG), and to develop classification models to facilitate its identification. Methods In this retrospective study, stroke patients admitted to the Department of Rehabilitation, Xuanwu Hospital, Capital Medical University between August 2022 and December 2024 were included and divided into VSN (n=22) and non-VSN (n=21) groups based on paper-and-pencil assessments. A healthy control group (n=20) was also recruited. Microstate segmentation and graph-theoretical analysis were applied to rEEG data to extract microstate parameters and topological network features. Four machine learning models (logistic regression, naïve Bayes, k-nearest neighbors, and decision tree) were built for classification. Results Compared with the non-VSN group, the VSN group showed significantly increased mean duration and time coverage in microstate C, and significantly decreased coverage and occurrence in microstate D. Graph-theoretical analysis revealed higher average clustering coefficients in the VSN group. Degree centrality in the frontal-central regions (C1, CZ) was significantly lower, while that in the parietal-occipital regions (P5, P3, PO7, PO5) was significantly higher than in the non-VSN group. Among the classification models, logistic regression and naïve Bayes models performed best, with the mean duration of microstate C contributing most to classification performance. Conclusions Patients with VSN exhibit distinct alterations in electroencephalography microstate dynamics and functional network topology. Microstate parameters play a crucial role in distinguishing VSN from non-VSN stroke cases, and combining these features with machine learning offers a promising approach for early identification and personalized intervention of VSN.
Epilepsy is a prevalent neurological disorder characterized by recurrent, transient episodes of central nervous system dysfunction resulting from abnormal neuronal discharges in the brain. Diagnosis of epilepsy integrates clinical manifestations, electroencephalogram (EEG) findings, and imaging studies. Clinical presentations are diverse and variable, with abnormal EEG serving as a critical diagnostic indicator; however, some patients exhibit normal EEG results. Moreover, there are still many patients who were underdiagnosed because of atypical epilepsy symptoms. With advancements in EEG and multimodal imaging technologies, diagnostic strategies based on biorhythm theory have emerged. This paper reviewed the diagnostic approaches for epilepsy grounded in biorhythm theory, in order to provide more effective support for the clinical management of epilepsy.
Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.
Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet’s 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.
ObjectiveNumerous foreign researches focused on the changes of EEG during the developmental periods from the newborn to late adulthood. However, the EEG changes of healthy Chinese people is still rare. Therefore, we examined the EEG of 2 357 healthy Chinese people.MethodsIn 1982, guided by Prof. Feng, we analysed the waking EEG of 2 357 healthy people, from 2 to above 60 years old, including open eyes induction test and hyperventilation.ResultsAt age 2 ~ 4, the posterior basic rhythms has reached 8 ~ 9 Hz, but the rhythms were unregular pattern. After age 7, the rhythms were 9 Hz, α index was more than 60%, the amplitude was higher than other ages. At age 12 ~ 14, the main rhythms was 10 Hz, the same as adulthood, α index was 70% ~ 80%. After this age, the amplitude of α rhythm deceased gradually. Above 60 years old, the main rhythm was 9 Hz, α index <60%, the amplitude was lower than adulthood. At age 14 ~ 16, the θ index in frontal and temporal regions was 6%, the same as the adulthood. At age 18 ~ 20, β index was 20%.ConclusionsIn the article, we analyzed the waking EEG of 2 357 healthy Chinese people in Beijing area. Although this multi-center study was accomplished at 1980s, the data is still of great value to the clinical EEG today.