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Alcoholic beverages flavour is complex and unique with different alcohol content, and the application of flavour perception could improve the objectivity of flavour evaluation. This study utilized electroencephalogram (EEG) to assess brain reactions to alcohol percentages (5 %-53 %) and Baijiu's complex flavours. The findings demonstrate the brain's proficiency in discerning between alcohol concentrations, evidenced by increasing physiological signal strength in tandem with alcohol content. When contrasted with alcohol solutions of equivalent concentrations, Baijiu prompts a more significant activation of brain signals, underscoring EEG's capability to detect subtleties due to flavour complexity. Additionally, the study reveals notable correlations, with δ and α wave intensities escalating in response to alcohol stimulation, coupled with substantial activation in the frontal, parietal, and right temporal regions. These insights verify the efficacy of EEG in charting the brain's engagement with alcoholic flavours, setting the stage for more detailed exploration into the neural encoding of these sensory experiences.

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The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

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BACKGROUND: The pathogenesis of HIV-associated neurocognitive (NC) impairment is multifactorial, and antiretroviral (ARV) neurotoxicity may contribute. However, interventional pharmacological studies are limited. METHODS: Single-blind, randomized (1:1), controlled trial to assess the change of NC performance (Global Deficit Score, GDS, and domain scores) in PLWH with NC impairment randomized to continue their standard of care treatment or to switch to a less neurotoxic ARV regimen: darunavir/cobicistat, maraviroc, emtricitabine (MARAND-X). Participants had plasma and cerebrospinal fluid HIV RNA< 50 copies/mL, R5-tropic HIV, and were on ARV regimens that did not include efavirenz and darunavir. The change of resting-state electroencephalography was also evaluated. The outcomes were assessed at week 24 of the intervention through tests for longitudinal paired data and mixed-effect models. RESULTS: Thirty-eight participants were enrolled and 28 completed the follow-up. Global Deficit Score improved over time but with no difference between arms in longitudinal adjusted models. Perceptual functions improved in the MARAND-X, while long-term memory improved only in participants within the MARAND-X for whom the central nervous system penetration-effectiveness (CNS penetration effectiveness) score increased by ≥3. No significant changes in resting-state electroencephalography were observed. CONCLUSIONS: In this small but well-controlled study, the use of less neurotoxic ARV showed no major beneficial effect over an unchanged regimen. The beneficial effects on the memory domain of increasing CNS penetration effectiveness score suggest that ARV neuropenetration may have a role in cognitive function.

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Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

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We know little about the mechanisms through which leader-follower dynamics during dyadic play shape infants' language acquisition. We hypothesized that infants' decisions to visually explore a specific object signal focal increases in endogenous attention, and that when caregivers respond to these proactive behaviors by naming the object it boosts infants' word learning. To examine this, we invited caregivers and their 14-mo-old infants to play with novel objects, before testing infants' retention of the novel object-label mappings. Meanwhile, their electroencephalograms were recorded. Results showed that infants' proactive looks toward an object during play associated with greater neural signatures of endogenous attention. Furthermore, when caregivers named objects during these episodes, infants showed greater word learning, but only when caregivers also joined their focus of attention. Our findings support the idea that infants' proactive visual explorations guide their acquisition of a lexicon.

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Definitions of human pain acknowledge at least two dimensions of pain, affective and sensory, described as separable and thus potentially differentially modifiable. Using electroencephalography, we investigated perceptual and neural changes of emotional pain modulation in healthy individuals. Painful electrical stimuli were applied after presentation of priming emotional pictures (negative, neutral, positive) and followed by pain intensity and unpleasantness ratings. We found that perceptual and neural event-related potential responses to painful stimulation were significantly modulated by emotional valence. Specifically, pain unpleasantness but not pain intensity ratings were increased when pain was preceded by negative compared to neutral or positive pictures. Amplitudes of N2 were higher when pain was preceded by neutral compared to negative and positive pictures, and P2 amplitudes were higher for negative compared to neutral and positive pictures. In addition, a hierarchical regression analysis revealed that P2 alone and not N2, predicted pain perception. Finally, source analysis showed the anterior cingulate cortex and the thalamus as main spatial clusters accounting for the neural changes in pain processing. These findings provide evidence for a separation of the sensory and affective dimensions of pain and open new perspectives for mechanisms of pain modulation.

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Near-death experience (NDE) is a transcendent mental event of uncertain etiology that arises on the cusp of biological death. Since the discovery of NDE in the mid-1970s, multiple neuroscientific theories have been developed in an attempt to account for it in strictly materialistic or reductionistic terms. Therefore, in this conception, NDE is at most an extraordinary hallucination without any otherworldly, spiritual, or supernatural denotations. During the last decade or so, a number of animal and clinical studies have emerged which reported that about the time of death, there may be a surge of high frequency electroencephalogram (EEG) at a time when cortical electrical activity is otherwise at a very low ebb. This oscillatory rhythm falls within the range of the enigmatic brain wave-labelled gamma-band activity (GBA). Therefore, it has been proposed that this brief, paradoxical, and perimortem burst of the GBA may represent the neural foundation of the NDE. This study examines three separate but related questions concerning this phenomenon. The first problem pertains to the electrogenesis of standard GBA and the extent to which authentic cerebral activity has been contaminated by myogenic artifacts. The second problem involves the question of whether agents that can mimic NDE are also underlain by GBA. The third question concerns the electrogenesis of the surge in GBA itself. It has been contended that this is neither cortical nor myogenic in origin. Rather, it arises in a subcortical (amygdaloid) location but is recorded at the cortex via volume conduction, thereby mimicking standard GBA. Although this surge of GBA contains genuine electrophysiological activity and is an intriguing and provocative finding, there is little evidence to suggest that it could act as a kind of neurobiological skeleton for a phenomenon such as NDE.

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Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

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Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens new possibilities for creative expression and experimentation. Notably, our results show that the generated images successfully reflect the dynamic and stochastic nature of the underlying neural activity, providing a unique procedure for visualization of brain function.

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Here, we characterized the dynamics of sleep spindles, focusing on their damping, which we estimated using a metric called oscillatory-Quality (o-Quality), derived by fitting an autoregressive model to electrophysiological signals, recorded from the cortex in mice. The o-Quality of sleep spindles correlates weakly with their amplitude, shows marked laminar differences and regional topography across cortical regions, reflects the level of synchrony within and between cortical networks, is strongly modulated by sleep-wake history, reflects the degree of sensory disconnection, and correlates with the strength of coupling between spindles and slow waves. As most spindle events are highly localized and not detectable with conventional low-density recording approaches, o-Quality thus emerges as a valuable metric that allows us to infer the spread and dynamics of spindle activity across the brain and directly links their spatiotemporal dynamics with local and global regulation of brain states, sleep regulation, and function.

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Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.

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Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

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This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

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Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

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Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

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Variations in interoceptive signals from the baroreceptors (BRs) across the cardiac and respiratory cycle can modulate cortical excitability and so affect awareness. It remains debated at what stages of processing they affect awareness-related event-related potentials (ERPs) in different sensory modalities. We investigated the influence of the cardiac (systole/diastole) and the respiratory (inhalation/exhalation) phase on awareness-related ERPs. Subjects discriminated visual threshold stimuli while their electroencephalogram, electrocardiogram, and respiration were simultaneously recorded. We compared ERPs and their intracranial generators for stimuli classified correctly with and without awareness as a function of the cardiac and respiratory phase. Cyclic variations of interoceptive signals from the BRs modulated both the earliest electrophysiological markers and the trajectory of brain activity when subjects became aware of the stimuli: an early sensory component (P1) was the earliest marker of awareness for low (diastole/inhalation) and a perceptual component (visual awareness negativity) for high (systole/exhalation) BR activity, indicating that BR signals interfere with the sensory processing of the visual input. Likewise, activity spread from the primary visceral cortex (posterior insula) to posterior parietal cortices during high and from associative interoceptive centers (anterior insula) to the prefrontal cortex during low BR activity. Consciousness is thereby resolved in cognitive/associative regions when BR is low and in perceptual centers when it is high. Our results suggest that cyclic fluctuations of BR signaling affect both the earliest markers of awareness and the brain processes underlying conscious awareness.

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Separation/conversion disorders in functional coma with pseudocataplexy are rare.On December 9,2021,a young female patient with separation/conversion disorders was treated in the Department of Neurology in the First Affiliated Hospital of Shandong First Medical University.The main symptoms were episodic consciousness disorders,sudden fainting,and urinary incontinence.Complete laboratory tests and cranial magnetic resonance imaging showed no obvious abnormalities.Standard multi-channel sleep monitoring and multiple sleep latency tests were performed.The patient was unable to wake up during nap and underwent stimulation tests.There was no response to orbital pressure,loud calls,or tapping,while the α rhythm in all electroencephalogram leads and the increased muscular tone in the mandibular electromyography indicated a period of wakefulness.The results of 24-hour sleep monitoring suggested that the patient had sufficient sleep at night and thus was easy to wake up in the morning.The results of daytime unrestricted sleep and wake-up test showed that the patient took one nap in the morning and one nap in the afternoon.When the lead indicated the transition from N3 to N2 sleep,a wake-up test was performed on the patient.At this time,the patient reacted to the surrounding environment and answered questions correctly.Because the level of orexin in the cerebrospinal fluid was over 110 pg/mL,episodic sleep disorder was excluded and the case was diagnosed as functional coma accompanied by pseudocataplexy.The patient did not present obvious symptom remission after taking oral medication,and thus medication withdrawl was recommended.Meanwhile,the patient was introduced to adjust the daily routine and mood.The follow-up was conducted six months later,and the patient reported that she did not experience similar symptoms after adjusting lifestyle.Up to now,no similar symptoms have appeared in multiple follow-up visits for three years.Functional coma with pseudocataplexy is prone to misdiagnosis and needs to be distinguished from true coma and episodic sleep disorders.

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BACKGROUND AND OBJECTIVES: Treatment-resistant depression is a leading cause of disability. Our center's trial for neurosurgical intervention for treatment-resistant depression involves a staged workup for implantation of a personalized, closed-loop neuromodulation device for refractory depression. The first stage ("stage 1") of workup involves implantation of 10 stereoelectroencephalography (SEEG) electrodes bilaterally into 5 anatomically defined brain regions and involves a specialized preoperative imaging and planning workup and a frame-based operating protocol. METHODS: We rely on diffusion tractography when planning stereotactic targets for 3 of 5 anatomic areas. We outline the rationale and fiber tracts that we focus on for targeting amygdala, ventral striatum and ventral capsule, and subgenual cingulate. We also outline frame-based stereotactic considerations for implantation of SEEG electrodes. EXPECTED OUTCOMES: Our method has allowed us to safely target all 5 brain areas in 3 of 3 trial participants in this ongoing study, with adequate fiber bundle contact in each of the 3 areas targeted using tractography. Furthermore, we ultimately used tractography data from our stage 1 workup to guide targeting near relevant fiber bundles for stage 2 (implantation of a responsive neuromodulation device). On completion of our data set, we will determine the overlap between volume of tissue activated for all electrodes and areas of interest defined by anatomy and tractography. DISCUSSION: Our protocol outlined for SEEG electrode implantation incorporates tractography and frame-based stereotaxy.

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