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Virtual reality (VR)–guided motor imagery (MI) is a widely used approach for motor rehabilitation, especially for patients with severe motor impairments. Most approaches provide visual guidance from the first‐person perspective (1PP). MI training with visual guidance from the third‐person perspective (3PP) remains largely unexplored. We argue that 3PP MI training has its own advantages and can supplement 1PP MI. For some movements beyond the view of 1PP, such as shoulder shrugging and other axial movements, MI are suitable performed under 3PP. However, the efficiency of existing paradigms for 3PP MI is unsatisfactory. We speculate that the absence of sense of body ownership (SOO) from 3PP could be one possible factor and hypothesize that 3PP MI could be enhanced by eliciting SOO over a 3PP avatar. Based on our hypothesis, a novel paradigm was proposed to enhance 3PP MI by inducing full‐body illusion (FBI) from 3PP, which is similar to the so‐called out‐of‐body experience (OBE), using synchronous visuo‐tactile stimulus with VR. The event‐related Electroencephalograph (EEG) desynchronization (ERD) at motor‐related regions from 31 healthy participants were calculated and compared with a control paradigm without "OBE" FBI induction. This study attempts to enhance 3PP MI with FBI induction. It offers an opportunity to perform MI guided by action observation from 3PP with elicited SOO to the observed avatar. We believe that 3PP MI could provide more possibilities for effective rehabilitation training, when SOO could be elicited to a virtual avatar and the present work demonstrates its viability and effectiveness. [ABSTRACT FROM AUTHOR]

The analysis and detection of sleep stages continue to preoccupy researchers, particularly bioinformaticians and neurologists aiming to understand various aspects and functioning of the brain during sleep cycles. This understanding is crucial for developing systems capable of early diagnosis of sleep disorders and adapting these systems for use in operating rooms to monitor the brain activity of patients under sedation or anesthesia. In this study, we apply a set of methods to process and decompose the single-channel EEG neural signal into different brain waves such as beta, alpha, theta, and delta. We then focus on the analysis and optimization of the extracted non-linear features using Principal Component Analysis (PCA) to improve the performance and robustness of the classification of six sleep stages. Classifier validation is performed using the cross-validation method. The results show that when the dimensions of the combined non-linear features are reduced, the RF-PCA model achieves an accuracy of 95.2%, outperforming other models such as MLP, SVM, DT, and GB, as well as recent studies. These findings validate the effectiveness of the system, indicating its potential for embedded implementation in practical applications for sleep disorder diagnosis and monitoring.

Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the “OBE” FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.

Background: Chemotherapy‐induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed‐loop brain–computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom report. Methods: Randomization to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1‐month data also were examined. Results: The BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, −2.60 vs. 0.38; 95% confidence interval, −3.67, −1.46 [p =.000; effect size, 1.07]; PLC, −2.26; 95% confidence interval, −3.33, −1.19 [p =.001 vs. WLC; effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group only. Conclusions: BCI is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long‐term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the intervention. Plain Language Summary: Chemotherapy‐induced nerve pain (neuropathy) can be disabling for cancer survivors; however, the way symptoms are felt depends on how the brain interprets the signals from nerves in the body.We determined that the perception of neuropathy can be changed by working directly with the brain.Survivors in our trial played 20 sessions of a type of video game that was designed to change the way the brain processed sensation and movement.In this, our second trial, we again observed significant improvement in symptoms that lasted after the treatment was complete. The current results demonstrated that a brain–computer interface was an effective treatment for chemotherapy‐induced peripheral neuropathy, with benefits maintained at the 1‐month follow‐up. Although scientifically interesting, the ability to separate real from placebo treatment may not be clinically helpful to obtain longer term significant relief for cancer survivors. [ABSTRACT FROM AUTHOR]

• A novel dynamic graph attention network is proposed for EEG emotion recognition. • The high accuracy can be obtained from the DGAT model in EEG emotion recognition. • The channel connectivity can reflect the synergistic relationships within the brain. Deep learning classification models based on electroencephalogram (EEG) emotion recognition have demonstrated considerable proficiency in the categorization of emotional states. However, these models have limitations in their capability to analyze the active states and cooperative relationships among distinct brain regions. This study proposes a dynamic graph attention network (DGAT) for EEG emotion recognition, which learns the features of each channel and leverages multiple-head self-attention mechanisms to capture non-Euclidean relationships between channels. Then, we use differential entropy features of emotions signals on the SJTU emotion EEG dataset (SEED). The DGAT model achieved improved subject-dependent and cross-subject classification accuracy compared to previous models. Moreover, ablation studies show that the channel weight matrix(CWM) and appropriate hyper-parameters can improve the performance of the DGAT model significantly. Furthermore, by conducting interpretable analysis of the new connections and electrode weights learned by the model, we find that these connection weight relationships reflect a certain degree of coordination within the brain for EEG-based emotion recognition. These findings provide a new method for EEG emotion recognition and highlights the potential for using deep learning models to analyze the active state and synergistic relationships among brain regions. [ABSTRACT FROM AUTHOR]

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could influence the work reported in this article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)