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.
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.
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.
ObjectiveTo investigate the clinical electrophysiological characteristics of Cyclin-dependent kinase-like 5 gene induced developmental epileptic encephalopathy (CDKL5-DEE). MethodsThe clinical data and series of video EEGs of children with CDKL5-associated developmental epileptic encephalopathy (CDKL5-DEE) who were admitted to the Children’s Medical Center of Peking University First Hospital from June 2016 to May 2024 were retrospectively analyzed. Results A total of 16 patients with CDKL5-DEE were enrolled, including 13 females and 3 males. All patients had de novo variants of CDKL5 gene, including 6 cases of missense variants, 5 cases of frameshift variants, 4 cases of nonsense variants, and 1 case of large fragment deletion. The age of onset was 8 days (d) after birth ~1 year (y) and 10 months (m), and the median age was (85.94±95.76) days. Types of seizures at onset: 4 cases of tonic seizures [age of onset 10~52 days, median age (25.5±15.84) days]; There were 5 cases of focal seizures [age of onset 8 d~8 m, median age (77.76±85.97) d]. There were 4 cases of epileptic spasmodic seizures [age of onset 3 m~1 y 10 m, median age (6.25±3.49) m]; There were 2 cases of bilateral tonic-clonic seizures [age of onset 30~40 days, median age (35.00±5.00) days]; focal concurrent epileptic spasm seizures 1 case (age of onset 2 m). A total of 59 VEEG sessions were performed in the pediatric EEG room of Peking University First Hospital for 4 hours. All the results were abnormal, including 26 normal background, 25 slow rhythm difference with background, and 8 no background. The interictal was 16 posterior or focal discharges, 19 multifocal discharges, 17 generalized or accompanied by focal/multifocal discharges, and 7 hypsarrhythmia; The ictal was 33 epileptic seizures, 6 myoclonic seizures, 5 focal seizures, 2 tonic-clonic seizures, 2 atypical absence seizures, 2 tonic seizures, 1 myoclonic sequential focal seizure, 1 focal sequential epileptic spasm, and 1 hypermotor-tonic-spasms. The background of patients within 6 months of age was normal, and the background abnormality increased significantly with age. generalized discharges are evident after 2 years of age between seizures. Conclusion CDKL5-DEE seizures have an early onset and are refractory to medications. Epileptic spasms are the most common type of seizure in every patient and long-lasting, with generalized seizures increasing markedly with age. EEG is characterized by a normal background within 6 months. With the increase of age, the background and interictal discharges have a tendency to deteriorate.
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.
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.
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.
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.
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.