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•Reviews the transfer learning methods in EEG signal analysis in recent years.•Discusses the challenges that transfer learning faces in the future development of EEG signal ...analysis.•Proposes the opportunities and application scenarios of transfer learning methods in EEG signal analysis.
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer interaction and neurological disease diagnosis, requires a large amount of labeled data for training. However, the collection of substantial EEG data could be difficult owing to its randomness and non-stationary. Moreover, there is notable individual difference in EEG data, which affects the reusability and generalization of models. For mitigating the adverse effects from the above factors, transfer learning is applied in this field to transfer the knowledge learnt in one domain into a different but related domain. Transfer learning adjusts models with small-scale data of the task, and also maintains the learning ability with individual difference. This paper describes four main methods of transfer learning and explores their practical applications in EEG signal analysis in recent years. Finally, we discuss challenges and opportunities of transfer learning and suggest areas for further study.
The study objective was to characterize preoperative and postoperative continuous electroencephalogram metrics and hemodynamic adverse events as predictors of neurodevelopment in congenital heart ...disease infants undergoing cardiac surgery.
From 2010 to 2021, 320 infants underwent congenital heart disease surgery at our institution, of whom 217 had perioperative continuous electroencephalogram monitoring and were included in our study. Neurodevelopment was assessed in 76 patients by the Bayley Scales of Infant and Toddler Development, 3rd edition, consisting of cognitive, communication, and motor scaled scores. Patient and procedural factors, including hemodynamic adverse events, were included by means of the likelihood of covariate selection in our predictive model. Median (25th, 75th percentile) follow-up was 1.03 (0.09, 3.44) years with 3 (1, 6) Bayley Scales of Infant and Toddler Development, 3rd Edition evaluations per patient.
Median age at index surgery was 7 (4, 23) days, and 81 (37%) were female. Epileptiform discharges, encephalopathy, and abnormality (lethargy and coma) were more prevalent on postoperative continuous electroencephalograms, compared with preoperative continuous electroencephalograms (P < .005). In 76 patients with Bayley Scales of Infant and Toddler Development, 3rd edition evaluations, patients with diffuse abnormality (P = .009), waveform discontinuity (P = .007), and lack of continuity (P = .037) on preoperative continuous electroencephalogram had lower cognitive scores. Patients with synchrony (P < .005) on preoperative and waveform continuity (P = .009) on postoperative continuous electroencephalogram had higher fine motor scores. Patients with postoperative adverse events had lower cognitive (P < .005) and gross motor scores (P < .005).
Phenotypic patterns of perioperative continuous electroencephalogram metrics are associated with late-term neurologic injury in infants with congenital heart disease requiring surgery. Continuous electroencephalogram metrics can be integrated with hemodynamic adverse events in a predictive algorithm for neurologic impairment.
Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. ...However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.
•A hybrid BCI real-time control system is developed to process BCI commands and drive an intelligent robot in real time.•A actor-critic based decision-making model is introduced to mitigate unconscious brain activities and minimize action errors.•The performance of the proposed BCI real-time control system is better than that of the state-of-the-art BCI systems.
Remimazolam is an ultrashort-acting benzodiazepine intravenous anesthetic that has recently been released. When using the total intravenous anesthesia technique, electroencephalogram（ECG）monitoring ...is recommended to maintain adequate depth of sedation. However, ECG during the administration of remimazolam sometimes differs from that of the previously standardized propofol in that beta waves are more likely to be observed, Burst Suppression is less likely to be detected, while higher processed ECG values are likely to be calculated. These factors make ECG monitoring challenging to interpret, so the condition of the patient must be comprehensively monitored when remimazolam is used.
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid ...detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.
We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation ...in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject.
In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.
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•A strategy for artifact rejection in M/EEG using peak-to-peak thresholds is proposed•The thresholds are estimated using cross-validation with a robust error metric•The method detects and repairs outlier data segments for each sensor•Comparison with competing methods on 200 subjects with ground truth responses
Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common ...neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.
Summary Autosomal dominant mutations S326fs328X and A322D in the GABA A receptor α1 subunit are associated with human absence epilepsy and juvenile myoclonic epilepsy, respectively. Because these ...mutations substantially reduce α1 subunit protein expression in vitro, it was hypothesized that they produce epilepsy by causing α1 subunit haploinsufficiency. However, in a mixed background strain of mice, α1 subunit deletion does not reduce viability or cause visually apparent seizures; the effects of α1 subunit deletion on electroencephalography (EEG) waveforms were not investigated. Here, we determined the effects of α1 subunit loss on viability, EEG spike‐wave discharges and seizures in congenic C57BL/6J and DBA/2J mice. Deletion of α1 subunit caused strain‐ and sex‐dependent reductions in viability. Heterozygous mice experienced EEG discharges and absence‐like seizures within both background strains, and exhibited a sex‐dependent effect on the discharges and viability in the C57BL/6J strain. These findings suggest that α1 subunit haploinsufficiency can produce epilepsy and may be a major mechanism by which the S326fs328X and A322D mutations cause these epilepsy syndromes.
Clozapine, a drug effective in treatment resistant schizophrenia, can modulate the brain’s electrical activity as measured by an electroencephalogram (EEG). Past reviews have focused on synthesizing ...literature related to epileptiform activity or rate of seizures in clozapine treated individuals. The aim of this review was to determine whether clozapine’s mediated effects on measurements related to neural oscillations can inform its therapeutic effects. Here, literature pertaining to studies that implemented pre-post designed investigations of clozapine and measured frequency characteristics of neural oscillations in individuals with schizophrenia were reviewed. The synthesis of findings suggests that while clozapine is associated with alterations in all neural oscillations, slower waves (delta and theta) are consistently increased in power by clozapine. We then further discuss potential mechanisms that may underlie these effects of clozapine. Future research can implement the findings of this review to motivate hypothesis-driven investigations into clozapine responsiveness biomarkers.
Background: Epilepsy in children causes memory problems in the learning process, so an early diagnosis of epilepsy is needed. The modality for determining the diagnosis of epilepsy is an ...electroencephalogram (EEG) examination. EEG recording results in epilepsy patients are epileptiform waves that can vary according to the type of epilepsy suffered. Objective: This study aims to determine the relationship of epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients. Methods: This study is an analytical study that uses secondary data in the form of medical records with cross sectional design. The research subjects were 106 patients taken with total sampling technique. Data collection is done by recording medical record data on the data collection form made by researchers. Correlation analysis between variables in this study used the Fisher test. Results: The results of this study indicate there is a relationship between epileptiform waves on the electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients, with p = 0.018 in 0.050 significance value. Conclusion: It can be concluded that there is a correlation between epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients.
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