Action Observation with Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding.

OBJECTIVE: The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding. METHODS: Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding. RESULTS: The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% ± 7.61% in the four-class scenario. CONCLUSION AND SIGNIFICANCE: We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.

Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.

Electro-absorption modulated 53 Gbps widely tunable laser based on half-wave V-coupled cavities.

In this paper, a 53 Gbps widely tunable transmitter is experimentally demonstrated for the first time, to our knowledge. An InGaAlAs/InP multiple-quantum-well (MQW) wafer is used with an identical layer structure for both the V-coupled cavity laser (VCL) and the electro-absorption modulator (EAM). The VCL uses a shallow-etched waveguide to reduce loss, while the EAM uses a deep-etched waveguide to increase the 3-dB modulation bandwidth. With the temperature varying from 19.5 to 30°C, the transmitter achieves wavelength tuning of 42 channels with a spacing of 100 GHz, corresponding to a tuning range of 32.6 nm from 1538.94 to 1571.54 nm. The static extinction ratio (ER) for all channels is higher than 14 dB. The measured 3-dB electro-optic (E0) bandwidth of the transmitter is over 40 GHz, which fits well with the calculated 3-dB bandwidth. At a fixed peak-to-peak driving voltage of 2.4 V, all channels exhibit clearly an open eye diagram with a 53 Gbps non-return-to-zero (NRZ) signal, while the dynamic ER is higher than 4.5 dB.

High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction.

In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification.

BACKGROUND: An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback. NEW METHOD: In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes. RESULTS: The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance. COMPARISON WITH EXISTING METHODS: Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder. CONCLUSION: The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.

Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces.

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.

Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG.

Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.

4*10 Gbps WDM communication system based on a tunable V-cavity semiconductor laser.

This paper is about the V-cavity tunable semiconductor laser with a 1550 nm band used as a transmitter to build a wavelength division multiplexing (WDM) optical fiber communication link. In the experiment, a 20 km optical fiber communication link with a reasonable eye diagram and low bit error rate (BER) transmitted by 40 Gbps can be established. The experimental results show that a single laser can achieve a wavelength tuning range of 25 nm, reach 32 channels at a 100 GHz frequency interval, and the average side mode suppression ratio (SMSR) is above 39 dB. The advantages and application potential of V-cavity tunable semiconductor laser (VCL) for wavelength routing in optical communication networking are verified by experiments.

A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control.

While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.

Spatio-temporal evolution of human neural activity during visually cued hand movements.

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Effects of heat shock protein 90 on complement activation in myocardial ischemia/reperfusion injury after pioglitazone preconditioning.

BACKGROUND: Heat shock protein 90 (HSP90) appears to have a pivotal function in ischemic preconditioning. Pioglitazone preconditioning (PioC) attenuates myocardial ischemia/reperfusion (I/R) injuries. OBJECTIVES: The current study aims to investigate the role of HSP90, complement C3 and C5a, and nuclear factor kappa-B (NF-κB) in PioC-induced cardioprotection. MATERIAL AND METHODS: A total of 80 rats were randomly categorized into 4 groups, as follows: sham, I/R, PioC, and PioC+HSP90 inhibitor geldanamycin (PioC+GA). The sham group rats had a thoracotomy, in which the ligature was passed by the heart with no ligation (150 min). The other 3 groups were exposed to ischemia (30 min) followed by reperfusion (2 h). In the PioC group, pioglitazone (3 mg/kg) was administered intravenously 24 h before ischemia. In the PioC+GA group, after being pretreated with pioglitazone, GA was administered (intraperitoneally, 1 mg/kg) 30 min before ischemia. Myocardial infarct sizes (ISs), apoptosis rates, creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), and cardiac troponin I (cTnI) serum levels were determined. The HSP90, C3, NF-κB, C5a, B-cell lymphoma-2 (Bcl-2), and Bax expression levels, as well as interleukin (IL)-1ß, IL-6, intercellular cell adhesion molecule-1 (ICAM-1), and tumor necrosis factor alpha (TNF-α) mRNA levels were measured. RESULTS: The myocardial ISs, serum CK-MB, cTnI and LDH levels, apoptosis rates, IL-1ß, IL-6, TNF-α, ICAM-1 release, as well as Bax, C5a, C3, and NF-κB protein expression were considerably lower in the PioC group than in the I/R group (p < 0.05). The Bcl-2 and HSP90 expression was higher in the PioC group than in the I/R group (p < 0.05). Geldanamycin inhibited the effects of PioC. These data strongly demonstrate that the PioC-induced is dependent upon HSP90 activity. CONCLUSIONS: The HSP90 is indispensable for PioC-mediated cardioprotection. The HSP90 attenuates I/R-induced ISs, apoptosis of cardiomyocytes and myocardial inflammation through C3, C5a and NF-κB activation inhibition.

BCI Control of a Robotic Arm Based on SSVEP With Moving Stimuli for Reach and Grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review.

A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed that four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago: deep learning methods, surface electromyogram decomposition and open-source databases. To conclude the review, we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.

Real-Time Hand Gesture Recognition by Decoding Motor Unit Discharges Across Multiple Motor Tasks From Surface Electromyography.

OBJECTIVE: Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance in human-machine interfaces such as gesture recognition and proportional control. However, neural decoding across multiple motor tasks and in real-time remains challenging, which limits its wide application. In this work, we proposed a real-time hand gesture recognition method by decoding motor unit (MU) discharges across multiple motor tasks ( 10) in a motion-wise way. METHODS: The EMG signals were first divided into numerous segments related to motions. The convolution kernel compensation algorithm was applied for each segment individually. The local MU filters, which indicate the MU-EMG correlation for each motion, were calculated iteratively in each segment and reused for global EMG decomposition to trace the MU discharges across motor tasks in real-time. The motion-wise decomposition method was applied on the high-density EMG signals recorded during twelve hand gesture tasks from eleven non-disabled participants. The neural feature of discharge count was extracted for gesture recognition based on five common classifiers. MAIN RESULTS: On average, 164 ±34 MUs were identified for twelve motions from each subject, with a pulse-to-noise ratio of 32.1 ±5.6 dB. The average time cost of EMG decomposition in a sliding window of 50 ms was less than 5 ms. The average classification accuracy using a linear discriminant analysis classifier was 94.6 ±8.1%, which was significantly higher than that of a time-domain feature called root mean square. The superiority of the proposed method was also validated with a previously published EMG database comprising 65 gestures. CONCLUSION AND SIGNIFICANCE: These results indicate the feasibility and superiority of the proposed method for MU identification and hand gesture recognition across multiple motor tasks, extending the potential applications of neural decoding in human-machine interfaces.

Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control.

The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average [Formula: see text] of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.

Exosomal HSP90 induced by remote ischemic preconditioning alleviates myocardial ischemia/reperfusion injury by inhibiting complement activation and inflammation.

BACKGROUND/AIMS: The activation of the complement system and subsequent inflammatory responses are important features of myocardial ischemia/reperfusion (I/R) injury. Exosomes are nanoscale extracellular vesicles that play a significant role in remote ischemic preconditioning (RIPC) cardioprotection. The present study aimed to test whether RIPC-induced plasma exosomes (RIPC-Exo) exert protective effects on myocardial I/R injury by inhibiting complement activation and inflammation and whether exosomal heat shock protein 90 (HSP90) mediates these effects. METHODS: Rat hearts underwent 30 min of coronary ligation followed by 2 h of reperfusion. Plasma exosomes were isolated from RIPC rats and injected into the infarcted myocardium immediately after ligation. Sixty rats were randomly divided into Sham, I/R, I/R + RIPC-Exo (50 µg/µl), and RIPC-Exo + GA (geldanamycin, 1 mg/kg, administration 30 min before ligation) groups. Cardiomyocyte apoptosis, the release of myocardial markers (LDH, cTnI and CK-MB), infarct size, the expression of HSP90, complement component (C)3, C5a, c-Jun N-terminal kinase (JNK), interleukin (IL)-1ß, tumor necrosis factor (TNF)-alpha and intercellular adhesion molecule -1 (ICAM-1) were assessed. RESULTS: RIPC-Exo treatment significantly reduced I/R-induced cardiomyocyte apoptosis, the release of myocardial markers (LDH, cTnI and CK-MB) and infarct size. These beneficial effects were accompanied by decreased C3 and C5a expression, decreased inflammatory factor levels (IL-1ß, TNF-α, and ICAM-1), decreased JNK and Bax, and increased Bcl-2 expression. Meanwhile, the expression of HSP90 in the exosomes from rat plasma increased significantly after RIPC. However, treatment with HSP90 inhibitor GA significantly reversed the cardioprotection of RIPC-Exo, as well as activated complement component, JNK signalling and inflammation, indicating that HSP90 in exosomes isolated from the RIPC was important in mediating the cardioprotective effects during I/R. CONCLUSION: Exosomal HSP90 induced by RIPC played a significant role in cardioprotection against I/R injury, and its function was in part linked to the inhibition of the complement system, JNK signalling and local and systemic inflammation, ultimately alleviating I/R-induced myocardial injury and apoptosis by the upregulation of Bcl-2 expression and the downregulation of proapoptotic Bax.

Light modulates glucose metabolism by a retina-hypothalamus-brown adipose tissue axis.

Public health studies indicate that artificial light is a high-risk factor for metabolic disorders. However, the neural mechanism underlying metabolic modulation by light remains elusive. Here, we found that light can acutely decrease glucose tolerance (GT) in mice by activation of intrinsically photosensitive retinal ganglion cells (ipRGCs) innervating the hypothalamic supraoptic nucleus (SON). Vasopressin neurons in the SON project to the paraventricular nucleus, then to the GABAergic neurons in the solitary tract nucleus, and eventually to brown adipose tissue (BAT). Light activation of this neural circuit directly blocks adaptive thermogenesis in BAT, thereby decreasing GT. In humans, light also modulates GT at the temperature where BAT is active. Thus, our work unveils a retina-SON-BAT axis that mediates the effect of light on glucose metabolism, which may explain the connection between artificial light and metabolic dysregulation, suggesting a potential prevention and treatment strategy for managing glucose metabolic disorders.

Neural evidence for functional roles of tactile and visual feedback in the application of myoelectric prosthesis.

Objective. The primary purpose of this study was to investigate the electrophysiological mechanism underlying different modalities of sensory feedback and multi-sensory integration in typical prosthesis control tasks.Approach. We recruited 15 subjects and developed a closed-loop setup for three prosthesis control tasks which covered typical activities in the practical prosthesis application, i.e. prosthesis finger position control (PFPC), equivalent grasping force control (GFC) and box and block control (BABC). All the three tasks were conducted under tactile feedback (TF), visual feedback (VF) and tactile-visual feedback (TVF), respectively, with a simultaneous electroencephalography (EEG) recording to assess the electroencephalogram (EEG) response underlying different types of feedback. Behavioral and psychophysical assessments were also administered in each feedback condition.Results. EEG results showed that VF played a predominant role in GFC and BABC tasks. It was reflected by a significantly lower somatosensory alpha event-related desynchronization (ERD) in TVF than in TF and no significant difference in visual alpha ERD between TVF and VF. In PFPC task, there was no significant difference in somatosensory alpha ERD between TF and TVF, while a significantly lower visual alpha ERD was found in TVF than in VF, indicating that TF was essential in situations related to proprioceptive position perception. Tactile-visual integration was found when TF and VF were congruently implemented, showing an obvious activation over the premotor cortex in the three tasks. Behavioral and psychophysical results were consistent with EEG evaluations.Significance. Our findings could provide neural evidence for multi-sensory integration and functional roles of tactile and VF in a practical setting of prosthesis control, shedding a multi-dimensional insight into the functional mechanisms of sensory feedback.

Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential.

In a realistic steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) application like driving a car or controlling a quadrotor, observing the surrounding environment while simultaneously gazing at the stimulus is necessary. This kind of application inevitably could cause head movements and variation of the accompanying gaze fixation point, which might affect the SSVEP and BCI's performance. However, few papers studied the effects of head movements and gaze fixation switch on SSVEP response, and the corresponding BCI performance. This study aimed to explore these effects by designing a new ball tracking paradigm in a virtual reality (VR) environment with two different moving tasks, i.e., the following and free moving tasks, and three moving patterns, pitch, yaw, and static. Sixteen subjects were recruited to conduct a BCI VR experiment. The offline data analysis showed that head moving patterns [F(2, 30) = 9.369, p = 0.001, effect size = 0.384] resulted in significantly different BCI decoding performance but the moving tasks had no effect on the results [F(1, 15) = 3.484, p = 0.082, effect size = 0.188]. Besides, the canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) accuracy were better than the PSDA and MEC methods in all of the conditions. These results implied that head movement could significantly affect the SSVEP performance but it was possible to switch gaze fixation to interact with the surroundings in a realistic BCI application.

Melanopsin retinal ganglion cells mediate light-promoted brain development.

During development, melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) become light sensitive much earlier than rods and cones. IpRGCs project to many subcortical areas, whereas physiological functions of these projections are yet to be fully elucidated. Here, we found that ipRGC-mediated light sensation promotes synaptogenesis of pyramidal neurons in various cortices and the hippocampus. This phenomenon depends on activation of ipRGCs and is mediated by the release of oxytocin from the supraoptic nucleus (SON) and the paraventricular nucleus (PVN) into cerebral-spinal fluid. We further characterized a direct connection between ipRGCs and oxytocin neurons in the SON and mutual projections between oxytocin neurons in the SON and PVN. Moreover, we showed that the lack of ipRGC-mediated, light-promoted early cortical synaptogenesis compromised learning ability in adult mice. Our results highlight the importance of light sensation early in life on the development of learning ability and therefore call attention to suitable light environment for infant care.