EEG Information Research Articles

Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

A pre-action indication of making stop/go decisions at flashing-light-controlled grade crossings based on drivers’ EEG information: A driving simulation experiment with audio-warning assistance

This study found that the electroencephalogram (EEG) information is a prior indication for sensing drivers’ imminent actions. It is possible to get to know the drivers’ intentions before their next moves, providing huge potentials of early interventions on the occurrence of accidents. A driving simulation experiment was conducted to identify EEG patterns during pre-action decision-making, assess their roles in stop/go decisions, and evaluate the impact of warnings on EEG signals. Four driving groups were divided based on drivers’ stop/go decisions and the influence of dilemma-zone effect, which could reflect drivers’ abilities to stop safely at the onset of flashing red light. The results revealed significant variations in EEG signals among the four driving groups, highlighting EEG as a sensitive pre-action indication. EEG changes indicated that with warning assistance, drivers have enhanced situational awareness, are able to make a stop/go decision in advance, and have less workload on visual search when approaching grade crossings. Such EEG patterns could interpret drivers’ stop/go decisions with a highest accuracy of 97.11%, and could identify potentially dangerous behaviors. These insights have implications for pre-event intervention based on EEG monitoring using non-contact devices.

CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition.

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson’s disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy.

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Investigation of the acute impact of rosemary consumption on brain activity in healthy volunteers

ABSTRACT Objectives Rosmarinus officinalis L. (rosemary) is a fragrant plant of the mint family, broadly known as a nourishment flavoring agent; it is additionally utilized in conventional people cures for its anti-inflammatory, diuretic, and antibacterial properties. Intense cognitive impacts from devouring plant-based flavonoids can be measured with electroencephalography (EEG), which records unconstrained brain movement. Brain activity can be evaluated amid independent states or whereas performing attentional assignments. This study aimed to determine the impact of rosemary consumption on cognitive consequences. Methods Twenty volunteers took part in the study. EEG was taken for each volunteer twice, before drinking rosemary extract and around one hour after drinking it. EEG information was recorded with a Micromed recording framework inspecting rate of 512 Hz. EEG signals were prepared to be utilized in EEGLAB, an open-source toolbox within the MATLAB environment. The information obtained after the EEG recording was compared with the preliminary EEG information. Results The signal’s power spectral density in theta, delta, and beta frequency bands modestly increased in males and females. Even though there was a significant increase in power at the alpha frequency band in both sexes, this increment was not specific channel-wise. Discussion The obtained data are consistent with the expected results and similar studies conducted, suggesting that the consumption of rosemary is beneficial for cognitive function in the short term. It is anticipated that forthcoming long-term studies will support the existing data.

A novel hybrid decoding neural network for EEG signal representation

In this paper, we proposed a novel hybrid decoding model that combines the superiority of CNNs and multi-head self-attention mechanisms, called HCANN, to finely characterizing EEG features. Depthwise separable convolution with multi-scale factors efficiently decouples temporal relevant information between brain-computer interface (BCI) tasks and EEG signals. Multi-head mechanism adaptively modified for EEG focuses on brain spatial activation patterns and extracts complementary spatial representation information from multiple subspaces. The proposed HCANN decodes the intent information of EEG recorded by three BCI paradigms, including one active and two passive BCI paradigms: rapid serial visual presentation, motor imagery, and imagined speech. We evaluated HCANN by comparing with the current state-of-the-art methods. The experimental results demonstrated that HCANN can effectively decode EEG and improves classification performance for all three BCI tasks. In addition, the visualization of spatial-temporal features at different decoding stages demonstrated that the proposed HCANN gradually extracts effective features related to the BCI tasks. The code of HCANN is publicly available at https://github.com/youshuoji/HCANN.

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

BackgroundSchizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required.New methodIn this study, we provide a classification approach for SZ patients based on a spatial–temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions.ResultsWe conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands.Comparison with existing methodsOther methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal.ConclusionWe provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

EEG analysis in patients with schizophrenia based on microstate semantic modeling method.

Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user’s intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

Permutation Entropy Does Not Track the Electroencephalogram-Related Manifestations of Paradoxical Excitation During Propofol-Induced Loss of Responsiveness: Results From a Prospective Observational Cohort Study.

During the anesthetic-induced loss of responsiveness (LOR), a "paradoxical excitation" with activation of β-frequencies in the electroencephalogram (EEG) can be observed. Thus, spectral parameters-as widely used in commercial anesthesia monitoring devices-may mistakenly indicate that patients are awake when they are actually losing responsiveness. Nonlinear time-domain parameters such as permutation entropy (PeEn) may analyze additional EEG information and appropriately reflect the change in cognitive state during the transition. Determining which parameters correctly track the level of anesthesia is essential for designing monitoring algorithms but may also give valuable insight regarding the signal characteristics during state transitions. EEG data from 60 patients who underwent general anesthesia were extracted and analyzed around LOR. We derived the following information from the power spectrum: (i) spectral band power, (ii) the spectral edge frequency as well as 2 parameters known to be incorporated in monitoring systems, (iii) beta ratio, and (iv) spectral entropy. We also calculated (v) PeEn as a time-domain parameter. We used Friedman's test and Bonferroni correction to track how the parameters change over time and the area under the receiver operating curve to separate the power spectra between time points. Within our patient collective, we observed a "paradoxical excitation" around the time of LOR as indicated by increasing beta-band power. Spectral edge frequency and spectral entropy values increased from 19.78 [10.25-34.18] Hz to 25.39 [22.46-30.27] Hz (P = .0122) and from 0.61 [0.54-0.75] to 0.77 [0.64-0.81] (P < .0001), respectively, before LOR, indicating a (paradoxically) higher level of high-frequency activity. PeEn and beta ratio values decrease from 0.78 [0.77-0.82] to 0.76 [0.73-0.81] (P < .0001) and from -0.74 [-1.14 to -0.09] to -2.58 [-2.83 to -1.77] (P < .0001), respectively, better reflecting the state transition into anesthesia. PeEn and beta ratio seem suitable parameters to monitor the state transition during anesthesia induction. The decreasing PeEn values suggest a reduction of signal complexity and information content, which may very well describe the clinical situation at LOR. The beta ratio mainly focuses on the loss of power in the gamma-band. PeEn, in particular, may present a single parameter capable of tracking the LOR transition without being affected by paradoxical excitation.

Sustained effort network for treatment of status epilepticus/European academy of neurology registry on adult refractory status epilepticus (SENSE-II/AROUSE)

BackgroundStatus Epilepticus (SE) is a common neurological emergency associated with a high rate of functional decline and mortality. Large randomized trials have addressed the early phases of treatment for convulsive SE. However, evidence regarding third-line anesthetic treatment and the treatment of nonconvulsive status epilepticus (NCSE) is scarce. One trial addressing management of refractory SE with deep general anesthesia was terminated early due to insufficient recruitment. Multicenter prospective registries, including the Sustained Effort Network for treatment of Status Epilepticus (SENSE), have shed some light on these questions, but many answers are still lacking, such as the influence exerted by distinct EEG patterns in NCSE on the outcome. We therefore initiated a new prospective multicenter observational registry to collect clinical and EEG data that combined may further help in clinical decision-making and defining SE.MethodsSustained effort network for treatment of status epilepticus/European Academy of Neurology Registry on refractory Status Epilepticus (SENSE-II/AROUSE) is a prospective, multicenter registry for patients treated for SE. The primary objectives are to document patient and SE characteristics, treatment modalities, EEG, neuroimaging data, and outcome of consecutive adults admitted for SE treatment in each of the participating centers and to identify factors associated with outcome and refractoriness. To reach sufficient statistical power for multivariate analysis, a cohort size of 3000 patients is targeted.DiscussionThe data collected for the registry will provide both valuable EEG data and information about specific treatment steps in different patient groups with SE. Eventually, the data will support clinical decision-making and may further guide the planning of clinical trials. Finally, it could help to redefine NCSE and its management.Trial registrationNCT number: NCT05839418.

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG.

To investigate mechanisms underlying loss of consciousness, it is important to extend methods established in humans to rodents as well. Perturbational complexity index (PCI) is a promising metric of "capacity for consciousness" and is based on a perturbational approach that allows inferring a system's capacity for causal integration and differentiation of information. These properties have been proposed as necessary for conscious systems. Measures based on spontaneous electroencephalography recordings, however, may be more practical for certain clinical purposes and may better reflect ongoing dynamics. Here, we compare PCI (using electrical stimulation for perturbing cortical activity) to several spontaneous electroencephalography-based measures of signal diversity and integrated information in rats undergoing propofol, sevoflurane, and ketamine anesthesia. We find that, along with PCI, the spontaneous electroencephalography-based measures, Lempel-Ziv complexity (LZ) and geometric integrated information (ΦG), were best able to distinguish between awake and propofol and sevoflurane anesthesia. However, PCI was anti-correlated with spontaneous measures of integrated information, which generally increased during propofol and sevoflurane anesthesia, contrary to expectations. Together with an observed divergence in network properties estimated from directed functional connectivity (current results) and effective connectivity (earlier results), the perturbation-based results seem to suggest that anesthesia disrupts global cortico-cortical information transfer, whereas spontaneous activity suggests the opposite. We speculate that these seemingly diverging results may be because of suppressed encoding specificity of information or driving subcortical projections from, e.g., the thalamus. We conclude that certain perturbation-based measures (PCI) and spontaneous measures (LZ and ΦG) may be complementary and mutually informative when studying altered states of consciousness.

EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving.

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power; and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median- and high-fatigue) with the EEG data transfer entropy. According to the results, only low- and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low- to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue.

Research Status and Development Trend of EEG Technology for Deep Learning in Emotion Recognition

EEG technology has made significant progress as people's attention to their emotions increases. This has also led to significant progress in various brain sciences, as well as their correlation with emotions. Nevertheless, based on the development of contemporary brain science, it is not possible to provide detailed cause analysis or draw accurate conclusions based on EEG information for a certain symptom or representation. The brain, as a complex organ of multiple entities, processes complex information by connecting multiple brain regions and clarifying the division of labor for each region. This creates a significant obstacle for the research method of brain computer interface, which uses computer algorithms to read information in the brain. Recently, many solutions and research methods have emerged to address this issue, such as electroencephalography (EEG), Positive Positron Emission Computed Tomography (PET) and Functional Magnetic Resolution Imaging (fMRI), etc. However, with such a large number of technologies, a method that can provide precise data from both temporal and spatial angles has not yet been derived. So in-depth research in biology, continuous exploration of computer algorithms can make outstanding contributions to the refinement of brain computer data. In summary, this article aims to provide an overview of the relationship between emotions and EEG analysis, as well as the principles of machine recognition and other applied technologies.

Novel algorithm for detection of cognitive dysfunction using neural networks

According to the World Health Organization (WHO), the number of cases of neurodegenerative diseases will double by 2030 and triple by 2050, becoming an alarming problem for the health systems. Cognitive dysfunction is one of the first symptoms of neurodegenerative diseases, so cognitive dysfunction detection contributes to the early detection of these diseases. On the other hand, the electroencephalogram (EEG) is a non-invasive test that records the electrical activity of the brain and has a wide field of applications in the medical field, one of which is the detection of cognitive dysfunction. The combination of signal processing tools, feature extraction, and artificial intelligence applied to EEG information allows the creation of helpful tools for the automatic detection of cognitive dysfunction. This work aimed to implement an efficient and robust computational system focused on processing EEG signals through Wavelets and the automatic classification of cognitive dysfunction using neural networks. The proposed method achieved an efficiency of 97.5% and an F1 of 98.42% using 10 electrodes for EEG recording.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length () (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient () and small-world index () did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps

Background:Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. New Methods:We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. Results:On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. Comparison with existing methods:In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. Conclusions:DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.

WEEGIT – software for visualization and annotation of the electrophysiological activity registration data

Wizard for EEG Information Tabling – “WEEGIT” is a software designed for visualization and annotation of the long-lasting records of activity. Long-lasting records of raw data are used in various areas of life sciences and well represented in neuroscience. In spite of development of other techniques and approaches the electrophysiological activity registration remains the “golden standard” in neurobiological research. Therefore, we developed and verified “WEEGIT” as a powerful and convenient tool for electrophysiological recordings’ description. We combined the most important features presented in other commercial and noncommercial software in the “WEEGIT”. Presented software can operate with widely used formats of records. ”WEEGIT” allows adaptive visualizing of up to 512 channels of record in different timescale without loss of efficiency and consumption of large machine resources. Visualizing also includes optional built-in temporal-spatial analysis (density of current sources or the density of action potentials) displayed as a background image. Integration of a set of optional filtering and signal transforming procedures allows improving record visualization. “WEEGIT” has a convenient graphical user interface with opportunity of simultaneous time browsing and annotating of the records in one workspace. Annotation can be done by simple text information typing as well as an interactive placing of specialized labels and objects. Software also allows saving data in the resampled format, both for the whole record and for user-defined events. In conclusion, “WEEGIT” provides a great set of benefits for the convenient workflow for beginners and specialists in the electrophysiological area of research, including preparation of the data for further specialized analysis.