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.
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.
Outcome-based education (OBE) emphasizes student learning outcomes as the core, utilizing a backward design approach to construct the curriculum. In teaching practice based on OBE, teachers need to develop a blueprint in advance that is closely aligned with the content of the teaching, aiming to promote deep learning and ensure that students can fully demonstrate their learning outcomes. Electroencephalogram (EEG) is a widely used technology in the field of neuroscience, and the special EEG changes convey a variety of information, which is crucial to the study of diseases. However, due to its specialization and learning difficulty, EEG teaching has been facing many challenges. Under the guidance of OBE concept, traditional knowledge lecture and problem-based learning (PBL) are organically integrated, combined with case analysis and flipped classroom teaching mode, which are applied in EEG teaching practice, in order to obtain more ideal teaching effect.
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.
Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer’s disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer’s disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.
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.
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.
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.
ObjectiveTo explore the clinical manifestation, diagnosis, treatment and prognosis of infantile spasm complicated with craniostenosis.MethodsA case of infantile spasm complicated with craniostenosis in the Department of Neurology of Qilu Children's Hospital in December 2017 was reviewed with the literature. The clinical manifestations, diagnosis, treatment and prognosis of infantile spasm with craniostenosis were analyzed.ResultsThe proband infantile spasms and craniostenosis was diagnosed by clinical, imaging examination and VEEG. Epileptic attack was prevented and craniostenosis was corrected by hormone shock therapy (corticotrophin was administered for 14 days, followed by topiramate)and surgical treatment (cranial cap reconstruction was performed), and good clinical prognosis was obtained.ConclusionThis case was the first reported case of craniostenosis with infantile spasm in China, and compared with the foreign treatment method, better treatment method and the operation opportunity were obtained. Which has a significant effect on the clinical treatment of infantile spasm complicated with transcranial disease.
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.