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Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial information from previous subjects, i.e., source domains. Nevertheless, EEG signals involve sensitive personal mental and health information. Thus, privacy concern becomes a critical issue. In addition, existing methods mostly assume that a portion of the new subject’s data is available and perform alignment or adaptation between the source and target domains. However, in some practical scenarios, new subjects prefer prompt BCI utilization over the time-consuming process of collecting data for calibration and adaptation, which makes the above assumption difficult to hold. To address the above challenges, we propose Online Source-Free Transfer Learning (OSFTL) for privacy-preserving EEG classification. Specifically, the learning procedure contains offline and online stages. At the offline stage, multiple model parameters are obtained based on the EEG samples from multiple source subjects. OSFTL only needs access to these source model parameters to preserve the privacy of the source subjects. At the online stage, a target classifier is trained based on the online sequence of EEG instances. Subsequently, OSFTL learns a weighted combination of the source and target classifiers to obtain the final prediction for each target instance. Moreover, to ensure good transferability, OSFTL dynamically updates the transferred weight of each source domain based on the similarity between each source classifier and the target classifier. Comprehensive experiments on both simulated and real-world applications demonstrate the effectiveness of the proposed method, indicating the potential of OSFTL to facilitate the deployment of BCI applications outside of controlled laboratory settings.

Previous studies have demonstrated that motor imagery leads to desynchronization in the alpha rhythm within the contralateral primary motor cortex. However, the underlying electrophysiological mechanisms responsible for this desynchronization during motor imagery remain unclear. To examine this question, we conducted an investigation using EEG in combination with noninvasive transcranial magnetic stimulation (TMS) during index finger abduction (ABD) and power grip imaginations. The TMS was administered employing diverse coil orientations to selectively stimulate corticospinal axons, aiming to target both early and late synaptic inputs to corticospinal neurons. TMS was triggered based on the alpha power levels, categorized in 20th percentile bins, derived from the individual alpha power distribution during the imagined tasks of ABD and power grip. Our analysis revealed negative correlations between alpha power and motor evoked potential (MEP) amplitude, as well as positive correlations with MEP latency across all coil orientations for each imagined task. Furthermore, we conducted functional network analysis in the alpha band to explore network connectivity during imagined index finger abduction and power grip tasks. Our findings indicate that network connections were denser in the fronto-parietal area during imagined ABD compared to power grip conditions. Moreover, the functional network properties demonstrated potential for effectively classifying between these two imagined tasks. These results provide functional evidence supporting the hypothesis that alpha oscillations may play a role in suppressing MEP amplitude and latency during imagined power grip. We propose that imagined ABD and power grip tasks may activate different populations and densities of axons at the cortical level.

Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method.

We have previously shown that healthy subjects can transfer coordination skills to the unpracticed hand by performing a unimanual task with the other hand and visualizing a bimanual action using a game-like interactive system. However, whether this system could be used to transfer coordination skills to the paretic hand after stroke and its underlying neural mechanism remain unknown. Here, using a game-like interactive system for visualization during physical practice in an immersive virtual reality environment, we examined coordination skill improvement in the unpracticed/paretic hand after training in 10 healthy subjects and 13 chronic and sub-acute stroke patients. The bimanual movement task was defined as simultaneously drawing non-symmetric three-sided squares (e.g., U and C), while the training strategy was performing a unimanual task with the right/nonparetic hand and visualizing a bimanual action. We found large decreases in the intra-hand temporal and spatial measures for movement in the unpracticed/paretic hand after training. Furthermore, a substantial reduction in the inter-hand temporal and spatial interference was observed after training. Additionally, we examined the related cortical network evolution using EEG in both the healthy subjects and stroke patients. Our studies show that the cortical network became more efficient after training in the healthy subjects and stroke patients. These results demonstrate that our proposed method could contribute to the transference of coordination skill to the paretic/unpracticed hand by promoting the efficiency of cortical networks.

Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting. We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.

Abstract Background The brain area stimulated during repetitive transcranial magnetic stimulation (rTMS) treatment is important in altered states of consciousness. However, the functional contribution of the M1 region during the treatment of high‐frequency rTMS remains unclear. Objective The aim of this study was to examine the clinical [the Glasgow coma scale (GCS) and the coma recovery scale‐revised (CRS‐R)] and neurophysiological (EEG reactivity and SSEP) responses in vegetative state (VS) patients following traumatic brain injury (TBI) before and after a protocol of high‐frequency rTMS over the M1 region. Methods Ninety‐nine patients in a VS following TBI were recruited so that their clinical and neurophysiological responses could be evaluated in this study. These patients were randomly allocated into three experimental groups: rTMS over the M1 region (test group; n = 33), rTMS over the left dorsolateral prefrontal cortex (DLPFC) (control group; n = 33) and placebo rTMS over the M1 region (placebo group; n = 33). Each rTMS treatment lasted 20 min and was carried out once a day. The duration of this protocol was a month with 20 treatments (5 times per week) occurring with that time. Results We found that the clinical and neurophysiological responses improved after treatment in the test, control, and placebo groups; the improvement was highest in the test group compared to that in the control and placebo groups. Conclusions Our results demonstrate an effective method of high‐frequency rTMS over the M1 region for consciousness recovery after severe brain injury.

Abstract Background The transfer of the behaviors of a human’s upper limbs to an avatar is widely used in the field of virtual reality rehabilitation. To perform the transfer, movement tracking technology is required. Traditionally, wearable tracking devices are used for tracking; however, these devices are expensive and cumbersome. Recently, non-wearable upper-limb tracking solutions have been proposed, which are less expensive and more comfortable. However, most products cannot track the upper limbs, including the arms and all the fingers at the same time, which limits the limb parts for tracking in a virtual environment and may lead to a limited rehabilitation effect. Methods In this paper, a novel virtual reality rehabilitation system (VRRS) was developed for upper-limb rehabilitation. The VRRS could track the motion of both upper limbs, integrate fine finger motion and the range of motion of the entire arm and map the motion to an avatar. To test the performance of VRRS, two experiments were designed. In the first experiment, we investigated the effect of VRRS on virtual body ownership, agency and location of the body and usability in 8 healthy participants by comparing it with a partial upper-limb tracking method based on a Leap Motion controller (LP) in the same virtual environments. In the second experiment, we examined the feasibility of VRRS in upper-limb rehabilitation with 27 stroke patients. Results VRRS improved the users’ senses of body ownership, agency, and location of the body. The users preferred using the VRRS to using the LP. In addition, we found that although the upper limb motor function of patients from all groups was improved, the difference between the FM scores tested on the first day and the last day of the experimental group was more significant than that of the control groups. Conclusions A VRRS with motion tracking of the upper limbs and avatar control including the arms and all the fingers was developed. It resulted in an improved user experience of embodiment and effectively improved the effects of upper limb rehabilitation in stroke patients. Trial registration The study was registered at the First Affiliated Hospital of Jinan University Identifier: KY-2020–036; Date of registration: June 01, 2020.

Objectives: Medial temporal lobe epilepsy (mTLE) is characterized by decreased hippocampal volume, which results in motor memory consolidation impairments. However, the extent to which motor memory acquisition are affected in humans with mTLE remains poorly understood. We therefore examined the extent to which learning of a motor tapping sequence task is affected by mTLE.Methods: MRI volumetric analysis was performed using a T1-weighted three-dimensional gradient echo sequence in 15 patients with right mTLE and 15 control subjects. Subjects trained on a motor sequence tapping task with the left hand in right mTLE and non-dominant hand in neurologically-intact controls.Results: The number of correct sequences performed by the mTLE patient group increased after training, albeit to a lesser extent than the control group. Although hippocampal subfield volume was reduced in mTLE relative to controls, no differences were observed in the volumes of other brain areas including thalamus, caudate, putamen and amygdala. Correlations between hippocampal subfield volumes and the change in pre- to post-training performance indicated that the volume of hippocampal subfield CA2–3 was associated with motor sequence learning in patients with mTLE.Significance: These results provide evidence that individuals with mTLE exhibit learning on a motor sequence task. Learning is linked to the volume of hippocampal subfield CA2–3, supporting a role of the hippocampus in motor memory acquisition.Highlights-Humans with mTLE exhibit learning on a motor tapping sequence task but not to the same extent as neurologically-intact controls.-Hippocampal subfield volumes are significantly reduced after mTLE. Surrounding brain area volumes do not show abnormalities.-Hippocampal subfield CA2–3 volume is associated with motor sequence learning in humans with mTLE.

Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.

Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., "old people" and "young people"), respectively, are obtained in the human brain.

Both the hippocampal and striatal systems participate in motor sequence learning (MSL) in healthy subjects, and the prominent role of the hippocampal system in sleep-related consolidation has been demonstrated. However, some pathological states may change the functional dominance between these two systems in MSL consolidation. To better understand the functional performance within these two systems under the pathological condition of hippocampal impairment, we compared the functional differences after consolidation between patients with left medial temporal lobe epilepsy (LmTLE) and healthy control subjects (HCs). We assessed participants' performance on the finger-tapping task (FTT) during acquisition (on day 1) and after consolidation during sleep (on day 2). All participants underwent an MRI scan (T1 and resting state) before each FTT. We found that the LmTLE group showed performance deficits in offline consolidation compared to the HC group. The LmTLE group exhibited structural changes, such as decreased gray matter volume (GMV) in the left hippocampus and increased GMV in the right putamen (striatum). Our results also revealed that whereas the main effect of consolidation was observed in the hippocampus-related functional connection in the HC group, it was only evident in the striatum-related functional loop in the LmTLE group. Our findings indicated that LmTLE patients may rely more on the striatal system for offline consolidation because of structural impairments in the hippocampus. Additionally, this compensatory mechanism may not fully substitute for the role of the impaired hippocampus itself. [ABSTRACT FROM AUTHOR]