Multi-scale analysis of acupuncture mechanisms for motor and sensory cortex activity based on SEEG data.

Acupuncture, a traditional Chinese therapy, is gaining attention for its impact on the brain. While existing electroencephalogram and functional magnetic resonance image research has made significant contributions, this paper utilizes stereo-electroencephalography data for a comprehensive exploration of neurophysiological effects. Employing a multi-scale approach, channel-level analysis reveals notable $\delta $-band activity changes during acupuncture. At the brain region level, acupuncture modulated connectivity between the paracentral lobule and the precentral gyrus. Whole-brain analysis indicates acupuncture's influence on network organization, and enhancing $E_{glob}$ and increased interaction between the motor and sensory cortex. Brain functional reorganization is an important basis for functional recovery or compensation after central nervous system injury. The use of acupuncture to stimulate peripheral nerve trunks, muscle motor points, acupoints, etc., in clinical practice may contribute to the reorganization of brain function. This multi-scale perspective provides diverse insights into acupuncture's effects. Remarkably, this paper pioneers the introduction of stereo-electroencephalography data, advancing our understanding of acupuncture's mechanisms and potential therapeutic benefits in clinical settings.

Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Evaluation of a manualised neurofeedback training in psychosomatic-psychotherapeutic outpatient treatment (Neuro-pp-out): study protocol for a clinical mixed-methods pilot study.

INTRODUCTION: Electroencephalographic neurofeedback (NFB), as a non-invasive form of brainwave training, has been shown to be effective in the treatment of various mental health disorders. However, only few results regarding manualised and standardised NFB trainings exist. This makes comparison as well as replication of studies difficult. Therefore, we developed a standard manual for NFB training in patients with mental health disorders attending a psychosomatic outpatient clinic. The current study aims at investigating the conduction of a standardised manual for NFB training in patients with mental health disorders. If successful, the study provides new opportunities to investigate NFB in a more controlled and comparable manner in clinical practice. METHODS AND ANALYSIS: 30 patients diagnosed with a mental health disorder will be included. After the educational interview, patients will undergo baseline diagnostics (T0). The subsequent intervention consists of 10 sessions of NFB training aiming at increasing sensorimotor rhythm and alpha-frequency amplitudes and decreasing theta-frequency and high beta-frequency amplitudes to induce relaxation and decrease subjective stress. All patients will undergo a post-treatment diagnostic assessment (T1) and a follow-up assessment 8 weeks following the closing session (T2). Changes in amplitude bands (primary outcome) will be recorded with electroencephalography during pre-assessments, post-assessments and follow-up assessments and during NFB sessions. Physiological (respiratory rate, blood volume pulse, muscle tension) and psychometric parameters (distress, perceived stress, relaxation ability, depressive and anxiety symptoms, insomnia, self-efficacy and quality of life) will be assessed at T0, T1 and T2. Moreover, satisfaction, acceptance and usability will be assessed at T1 after NFB training. Further, qualitative interviews about the experiences with the intervention will be conducted with NFB practitioners 6 months after the study starts. Quantitative data will be analysed using repeated measures analysis of variance as well as mediation analyses on mixed linear models. Qualitative data will be analysed using Mayring's content analysis. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of the Medical Faculty of the University of Duisburg-Essen (23-11140-BO) and patient enrolment began in April 2023. Before participation, written informed consent by each participant will be required. Results will be published in peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER: Prospectively registered on 28 March 2023 in the German clinical trials register, DRKS00031497.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network.

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates.

Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.

The effects of neurofeedback training for children with cerebral palsy and co-occurring attention deficits: A pilot study.

BACKGROUND: Limited research exists regarding the effectiveness of electroencephalogram (EEG) neurofeedback training for children with cerebral palsy (CP) and co-occurring attention deficits (ADs), despite the increasing prevalence of these dual conditions. This study aimed to fill this gap by examining the impact of neurofeedback training on the attention levels of children with CP and AD. METHODS: Nineteen children with both CP and co-occurring ADs were randomly assigned to either a neurofeedback or control group. The neurofeedback group received 20 sessions of training, lasting approximately 1 h per day, twice a week. Theta/beta ratios of the quantitative electroencephalography (QEEG) recordings were measured pre-training and post-training in the resting state. The Continuous Performance Test (CPT), the Test of Visual Perceptual Skills-3rd Version (TVPS-3) and the Conners' Parent Rating Scale (CPRS) were measured at pre- and post-training. RESULTS: The neurofeedback group showed both decreased theta/beta ratios compared with control group (p = 0.04) at post-training and a within-group improvement during training (p = 0.02). Additionally, the neurofeedback group had a trend of decreased omission rates of the CPT (p = 0.08) and the visual sequential memory and the visual closure subscores in the TVPS-3, compared with the control group (p = 0.02 and p = 0.01, respectively). CONCLUSIONS: The results suggested that children with CP and co-occurring AD may benefit from neurofeedback training in their attention level. Further research is needed to explore long-term effects and expand its application in this population.

Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network.

Selective attention-related top-down modulation plays a significant role in separating relevant speech from irrelevant background speech when vocal attributes separating concurrent speakers are small and continuously evolving. Electrophysiological studies have shown that such top-down modulation enhances neural tracking of attended speech. Yet, the specific cortical regions involved remain unclear due to the limited spatial resolution of most electrophysiological techniques. To overcome such limitations, we collected both electroencephalography (EEG) (high temporal resolution) and functional magnetic resonance imaging (fMRI) (high spatial resolution), while human participants selectively attended to speakers in audiovisual scenes containing overlapping cocktail party speech. To utilise the advantages of the respective techniques, we analysed neural tracking of speech using the EEG data and performed representational dissimilarity-based EEG-fMRI fusion. We observed that attention enhanced neural tracking and modulated EEG correlates throughout the latencies studied. Further, attention-related enhancement of neural tracking fluctuated in predictable temporal profiles. We discuss how such temporal dynamics could arise from a combination of interactions between attention and prediction as well as plastic properties of the auditory cortex. EEG-fMRI fusion revealed attention-related iterative feedforward-feedback loops between hierarchically organised nodes of the ventral auditory object related processing stream. Our findings support models where attention facilitates dynamic neural changes in the auditory cortex, ultimately aiding discrimination of relevant sounds from irrelevant ones while conserving neural resources.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Speech-induced suppression during natural dialogues.

When engaged in a conversation, one receives auditory information from the other's speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another's speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.

The effect of EEG neurofeedback on lowering the stress reaction level depending on various stressors on the biochemical, muscular and psychomotor sphere: A preliminary randomized study.

BACKGROUND: The human body is exposed to stressors on a daily basis. Short-term exposure to a particular stressor can cause the release of inflammatory markers - including c-reactive protein (CRP). EEG neurofeedback is a noninvasive form of therapy that aims to improve brain function. Neurofeedback is a type of feedback based on brain activity. METHODS: The research process was performed by a group of 80 men who were divided into 2 research groups and 2 control groups. In the first research group and the first control group, the stressor of high temperature was applied, while in the second research group and the second control group, the stressor was physical exertion to refusal. Meanwhile, blood samples were taken to visualize inflammatory markers. These were taken before and after the stressor, as well as before and after the application of EEG neurofeedback. RESULTS: In research group after EEG neurofeedback intervention, the level of CRP significantly dropped in the measurement after stressor implementation. Analysis of the SMK test revealed a significant influence of both factors (time F = 13.525, P = .035; state F = 10.658, P = .047) and their interaction (F = 16.709, P = .026). Same statically significant decrease was observed in the level of rect. abdom. EMG was after physical work after neurofeedback. In all tests, a decrease in the EMG amplitude of upper trap. was observed after physical work before neurofeedback with its further increase after neurofeedback. After neurofeedback training, the results of the 3 tested parameters of the sensorimotor coordination test performed after the second heat stressor improved statistically insignificantly compared to the values obtained before. CONCLUSION: This article shows the effect of EGG neurofeedback on reducing the negative effects of stress exposure in humans. The study showed that the level and a pattern of EEG neurofeedback influence and significance is different depending on the applied stressor. Furthermore, the level of EEG neurofeedback influence and significance in decreasing the stressor effect is different depending on the examined sphere.

Altered hierarchical auditory predictive processing after lesions to the orbitofrontal cortex.

Orbitofrontal cortex (OFC) is classically linked to inhibitory control, emotion regulation, and reward processing. Recent perspectives propose that the OFC also generates predictions about perceptual events, actions, and their outcomes. We tested the role of the OFC in detecting violations of prediction at two levels of abstraction (i.e., hierarchical predictive processing) by studying the event-related potentials (ERPs) of patients with focal OFC lesions (n = 12) and healthy controls (n = 14) while they detected deviant sequences of tones in a local-global paradigm. The structural regularities of the tones were controlled at two hierarchical levels by rules defined at a local (i.e., between tones within sequences) and at a global (i.e., between sequences) level. In OFC patients, ERPs elicited by standard tones were unaffected at both local and global levels compared to controls. However, patients showed an attenuated mismatch negativity (MMN) and P3a to local prediction violation, as well as a diminished MMN followed by a delayed P3a to the combined local and global level prediction violation. The subsequent P3b component to conditions involving violations of prediction at the level of global rules was preserved in the OFC group. Comparable effects were absent in patients with lesions restricted to the lateral PFC, which lends a degree of anatomical specificity to the altered predictive processing resulting from OFC lesion. Overall, the altered magnitudes and time courses of MMN/P3a responses after lesions to the OFC indicate that the neural correlates of detection of auditory regularity violation are impacted at two hierarchical levels of rule abstraction.

The Effects of Immersion and Visuo-Tactile Stimulation on Motor Imagery in Stroke Patients are Related to the Sense of Ownership.

Visual guided motor imagery (MI) is commonly used in stroke rehabilitation, eliciting event-related desynchronization (ERD) in EEG. Previous studies found that immersion level and visuo-tactile stimulation could modulate ERD during visual guided MI, and both of two factors could also improve sense of ownership (SOO) over target limb (or body). Additionally, the relationship was also reported between the performance of MI and SOO. This study aims to investigate whether immersion and visuo-tactile stimulation affect visual guided MI through the SOO over virtual body in stroke patients. Nineteen stroke patients were recruited. The experiment included two phases (i.e., SOO induction and visual guided MI with SOO) that was manipulated across four conditions in a within-subject design: 2×2 , i.e., immersion (VR, 2D monitor display) × multisensory stimulation (visuo-tactile stimulation, observation without tactile stimulation). Results found peaks ERD amplitude during MI were significantly higher in stronger SOO conditions than weaker SOO conditions. Interestingly, the ERD during visual guided MI under the condition of vision only in VR and visuo-tactile stimulation in 2D monitor are similar, which indicates that SOO may be an important factor behind this phenomenon (due to the similar SOO between these two conditions). A moderate correlation was also found between SOO scores and peaks ERD amplitude during MI. This study discussed the possible factor underlying the effects of immersion and multisensory stimulation on visual guided MI in post-stroke patients, identifying the effect of SOO in this process, and could be referred in future studies for coming up with better MI paradigms for stroke rehabilitation.

Effect of musical expertise on the perception of duration and pitch in language: A cross-linguistic study.

This study adopts a cross-linguistic perspective and investigates how musical expertise affects the perception of duration and pitch in language. Native speakers of Chinese (N = 44) and Estonian (N = 46), each group subdivided into musicians and non-musicians, participated in a mismatch negativity (MMN) experiment where they passively listened to both Chinese and Estonian stimuli, followed by a behavioral experiment where they attentively discriminated the stimuli in the non-native language (i.e., Chinese to Estonian participants and Estonian to Chinese participants). In both experiments, stimuli of duration change, pitch change, and duration plus pitch change were discriminated. We found higher behavioral sensitivity among Chinese musicians than non-musicians in perceiving the duration change in Estonian and higher behavioral sensitivity among Estonian musicians than non-musicians in perceiving all types of changes in Chinese, but no corresponding effect was found in the MMN results, which suggests a more salient effect of musical expertise on foreign language processing when attention is required. Secondly, Chinese musicians did not outperform non-musicians in attentively discriminating the pitch-related stimuli in Estonian, suggesting that musical expertise can be overridden by tonal language experience when perceiving foreign linguistic pitch, especially when an attentive discrimination task is administered. Thirdly, we found larger MMN among Chinese and Estonian musicians than their non-musician counterparts in perceiving the largest deviant (i.e., duration plus pitch) in their native language. Taken together, our results demonstrate a positive effect of musical expertise on language processing.

Parsing state mindfulness effects on neurobehavioral markers of cognitive control: A within-subject comparison of focused attention and open monitoring.

Over the past two decades, scientific interest in understanding the relationship between mindfulness and cognition has accelerated. However, despite considerable investigative efforts, pervasive methodological inconsistencies within the literature preclude a thorough understanding of whether or how mindfulness influences core cognitive functions. The purpose of the current study is to provide an initial "proof-of-concept" demonstration of a new research strategy and methodological approach designed to address previous limitations. Specifically, we implemented a novel fully within-subject state induction protocol to elucidate the neurobehavioral influence of discrete mindfulness states-focused attention (FA) and open monitoring (OM), compared against an active control-on well-established behavioral and ERP indices of executive attention and error monitoring assessed during the Eriksen flanker task. Bayesian mixed modeling was used to test preregistered hypotheses pertaining to FA and OM effects on flanker interference, the stimulus-locked P3, and the response-locked ERN and Pe. Results yielded strong but unexpected evidence that OM selectively produced a more cautious and intentional response style, characterized by higher accuracy, slower RTs, and reduced P3 amplitude. Follow-up exploratory analyses revealed that trait mindfulness moderated the influence of OM, such that individuals with greater trait mindfulness responded more cautiously and exhibited higher trial accuracy and smaller P3s. Neither FA nor OM modulated the ERN or Pe. Taken together, our findings support the promise of our approach, demonstrating that theoretically distinct mindfulness states are functionally dissociable among mindfulness-naive participants and that interactive variability associated with different operational facets of mindfulness (i.e., state vs. trait) can be modeled directly.

Increased Thalamic Connectivity in Juvenile Myoclonic Epilepsy Based on Electroencephalography Source-Level Analysis.

Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.

Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism.

Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.

Influence of a placebo tDCS treatment on cybersickness and EEG-neurofeedback success.

Virtual Reality (VR) serves as a modern and powerful tool within the domain of neurofeedback (NF). Users can learn how to alter their own brain activation with the help of NF, for example visual feedback. VR can help to make the training more engaging and motivating with its immersive nature. However, cybersickness (CS) poses a serious problem, as it negatively affects up to 80% of all VR users. Especially women seem to be affected. Some studies suggest positive effects of placebo interventions, so that less CS in the users can be detected. Hence, we investigated whether a transcranial direct current stimulation (tDCS) placebo intervention can influence CS symptoms in a VR-based NF training and whether CS affects NF performance. Additionally, we focused on possible sex differences in the development of CS and the NF success. For this purpose, we tested 41 healthy participants in an EEG-NF-training with sensorimotor rhythm (SMR, 12-15 Hz) upregulation and VR feedback. Half of the participants got a placebo tDCS stimulation in advance to the training and were told that the stimulation would prevent them from getting cybersick. The other half received no such treatment. Both groups underwent six NF runs to three minutes each where they were asked to follow a ball along a predefined path in the virtual environment by increasing their SMR. Results showed that women experienced significantly more CS than men regardless of whether they received a placebo intervention or not. Women were also not able to increase their SMR successfully over the six NF runs. Male participants were able to increase their SMR. Also, only participants in the non-placebo group were able to increase their SMR, not those from the placebo group. The tDCS placebo intervention had little to no effect on sickness symptoms in VR, however it hampered the ability to increase SMR power. Also, CS seems to be associated with a worse NF training outcome, especially in women. Strategies to reduce CS inducing factors in VR environments could help participants to benefit more from a VR-based NF training. This should be especially considered in vulnerable groups that are more prone to CS.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting.

Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.