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Laparoscopic skill involves sensory processing and motor control, which is associated with high-level alpha oscillation of the brain. Neurofeedback (NF) has been reported effective in enhancing alpha oscillation. Our objectives were to assess the alpha oscillation during laparoscopic skills training, and to verify the usefulness of NF in improving the learning efficacy. Sixty medical students without laparoscopic experience were recruited. Multi-channel electroencephalography (EEG) signals were recorded during training of peg transfer task. Training performance was assessed based on the task completion time. All subjects participated in the first experiment comprising eight training blocks and one testing block. Subjects were ranked based on performance: the top 20 subjects were classified as the good performance group and the bottom 20 subjects as the fair performance group. In the second experiment, the fair performance group were randomly divided into the NF and control groups. Spectral analysis of EEG signals was used to calculate alpha power and alpha band coherence. Training performance and EEG alpha powers were compared between the NF and control groups. In the first experiment, the completion time was significantly faster in the good performance group (62.5 ± 2.8 s) compared with the fair performance group (75.0 ± 5.6 s) (P

Brain death is the irreversible loss of all the functions of the brain and brainstem. Compared to traditional diagnostic methods of brain death, near-infrared spectroscopy (NIRS) is a noninvasive, objective, cost-effective, and safe way of assessment of brain death. Eighteen brain dead patients and 20 healthy subjects were studied by NIRS, with a multiple-phase protocol at varied fractions of inspired O

Brain death is the irreversible loss of all the functions of the brain and brainstem. Compared to traditional diagnostic methods of brain death, near-infrared spectroscopy (NIRS) is a noninvasive, objective, cost-effective, and safe way of assessment of brain death. Eighteen brain dead patients and 20 healthy subjects were studied by NIRS, with a multiple-phase protocol at varied fractions of inspired O2 (FIO2). We found that the changes in the concentration ratios of oxyhemoglobin to deoxyhemoglobin (Δ[HbO2]/Δ[Hb]) in the cerebral cortex of brain dead patients were significantly higher than those of healthy subjects, and its low-to-high FIO2 phase was most sensitive, with a recommended threshold in the range 1.40-1.50. Our study indicated that NIRS is a promising technology for assessing brain death. The success of this application potentially offers a supplementary technique for the assessment of brain death in real time in order to be able to promptly offer quality-assured donor organs.

Objective Using the Twente Medical Systems international B.V. (TMSi) electromyography (EMG) system, active signal shielding was applied to clean signals and create data without interference and cable movement artifacts. TMSi, used in high-density surface EMG pattern recognition, controls myoelectric rehabilitation robots, yet few have studied how active signal shielding influences pattern recognition. This study aimed to investigate how active signal shielding used within the TMSi influenced motion pattern recognition. Methods Surface EMG of dominant side forearm and hand muscles was studied in eight healthy participants. The common component’s influence was accessed by the classification performance of wrist and hand functional movements. Results The classification performance of EMG signals with the common component was obviously lower than signals without the common component using one to five electrodes. Conversely, a higher motion classification performance was achieved using signals with the common component using over 12 electrodes. Optimal channel distribution was examined based on the sequential feed-forward selection method, showing that the common component affected the optimal channel location. Conclusions Active signal shielding in the TMSi improved classification accuracy in motion pattern recognition when over 12 electrodes were used. The optimal channel distribution was related to the common component when using the TMSi.

P300 component of event related potentials in response to visual and auditory stimulation has been widely used in brain–computer interfaces (BCI). In clinical applications, tactile stimulus based on somatosensory electrical stimulation is an alternative for patients with impaired vision or hearing. This study presents an online P300 BCI based on somatosensory electrical stimulation paradigm. P300 signals were elicited by tactile selective attention of electrical stimuli on four fingers. Fifteen healthy subjects participated in this study. Participants’ task was to focus their attention on the target finger and count the number. The classification of P300 signals was performed by step-wise linear discriminate analysis. The average classification accuracy of the somatosensory BCI was 79.81 ± 7.91%, with the information transfer rate at 4.9 ± 1.3 bits/min. The BCI performance on different time windows was also evaluated in the present study. Our results demonstrate the feasibility of employing somatosensory electrical stimuli to build a practical online P300 BCI without taxing the visual and auditory channel, providing a wider application prospect in clinical applications and daily life. We anticipate our diagram to be a starting point for more explorations on utilizing electrical somatosensory stimuli in conjunction with portable BCI for neural rehabilitation.

Electrical activity of developing dissociated neuronal networks is of immense significance for understanding the general properties of neural information processing and storage. In addition, the complexity and diversity of network activity patterns make them ideal candidates for developing novel computational models and evaluating algorithms. However, there are rare databases which focus on the changing network dynamics during development. Here, we describe the design and implementation of Neuroinformation Database for Developing Networks (NDDN), a repository for electrophysiological data collected from long-term cultured hippocampal networks. The NDDN contains over 15 terabytes of multielectrode array data consisting of 25,380 items collected from 105 culture batches. Metadata including culturing and recording information and stimulation/drug application protocols are linked to each data item. A Matlab toolbox named MEAKit is also provided with the NDDN to ease the analysis of downloaded data items. We expect that NDDN may contribute to both the fields of experimental and computational neuroscience.

Intelligent wheelchairs working in various environments need to sense the neighborhood around itself and prevent dangerous situation such as collision and falling down from stairs. In this paper, we develop a low cost and real time intelligent wheelchair shared control system for people with disabilities at the rehabilitation center or at home. Our system is composed of an electric wheelchair, an RGB camera, an infrared camera, 4 ultrasonic sensors, a laser LiDAR (light detection and ranging equipment), and a personal computer. Based on the intelligent sensor network and sensor priority control algorithms, it can detect different obstacles with various dangerous levels, send out voice and graphical alarm, and suggest a safe path to avoid them. The combination usage of the RGB camera and the IR camera enables our system to work under normal lighting conditions and under low lighting conditions. The reliability of individual type of sensors and the whole system was examined by simulated scenarios and user questionnaire. Experiment results reveal that the system is reliable and effective. The key components in our system are low cost and easy to be installed on various electric wheelchairs and common PCs. It will be a useful tool for further developing smart wheelchairs with shared control frameworks.

Somatosensory evoked potentials (SEPs) were found to exhibit different time-frequency patterns after acute spinal cord injury (SCI) at different levels, which implies that changes of these patterns may be associated with the location of SCI. Based on this finding, we propose the hypothesis that there are information regarding the location of SCI contained in the time-frequency patterns of SEPs. Purpose of the present study is to verify this hypothesis by comparing the time-frequency patterns of SEPs after acute and chronic SCI at the same level. The study examined the distribution patterns of the time-frequency components (TFCs) of SEPs before and after acute and chronic injury at C5 level in the spinal cord. Experimental results of SEP recordings from 24 adult rats show that there are common areas in the time-frequency distributions of SEPs. The TFCs from both the acute injury group and the chronic injury group are located in these areas with no TFCs from the normal group. Findings suggest that these areas are likely to possess information concerning the site of neurological deficits in spinal cord while independent of the modality of injury. This study provides basis for identification of stable time-frequency patterns of SEPs after different types and locations of SCI, which will guide the development of SEP-based SCI location detection.

Brain death is a permanent loss of all brain function [1]. Current clinical organ transplantations mostly depend on the organs from brain-dead patients [2]. And of note, a lot of blood deases are easy to cause cerebral haemorrhage, which is quite of danger and usually induce brain death if not detected and treated in time. Thus prompt evaluation of brain death is of great significance for saving medical resources and reducing economic burden of the patients' families. Current guide for diagnosing brain death required to perform a list of >30 hours neurological examninations, some of which are even invasive, not in time and easily hampered by many confounding factors. An ideal ancillary test to assess brain death is highlighted to be noninvasive, sensitive, universally available, timely, and easy to perform at the bedside. Near infrared spectroscopy ( NIRS ) is capable of monitoring hemodynamics in response to brain activity noninvasively, conveniently, continually, and relatively inexpensively, evidented by a series of clinical cerebral studies recently. Weigl et al newly reported to use a time resolved NIRS to detect the fluorescence photons excited in the indocyanine green ( ICG ) for cerebral perfusion detection. It provided a novel optical ancillary tool to assess brain death, while its accuracy was only 69.2%, which did not reach the level of brain death confirmation. Plus, it was invasive, requiring injection of optical contrast agent. We attempted to assess brain death completely in nonivasive way with just a custom wearable NIRS device developed in our lab [3] ( fig.1 a ). We novelly incororate a protocol at markedly but safely varied fractions of oxygen respiration. Firstly, Monte Carlo modeling were carried out to test the difference in photon transport within human brain at different oxygen concentrations induced by varied fractions of oxygen respiration ( FIO2 ) [4]. 18 healthy subjects ( 41 ± 11 years old ) and 17 brain dead patients were recruited from the intensive care unit (ICU) in Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital. No significant difference in age was found between patients and healthy groups ( p >0.413 ). These patients were finally clinically diagnosed by the international standards of brain death. Two protocols were used ( fig.1 b). One is consisted of 1 hour resting, 3-minute baseline measure, half-hour measurement at 60% FIO2 ( phase I, high oxygen ),a half hour measure at 40% FIO2 ( phase II, low oxygen ), and a half hour measure at 60% FIO2 ( phase III, high oxygen ). The other is low, high, and low. The Δ[Hb] and Δ[HbO2] time courses were recorded by NIRS in real time with related signal processing ( fig.1 c ). Statistical analysis were focus on the sensitivity and specificiy of our proposed methodology at combination of NIRS and above protocol, as well as which protocol act better. Fig.1 ( c right ) showed that the detected light signal profile dramatically differed among varied oxygen concentrations in human brain. Plus the hemodynamic responses varied clearly between two subject groups among varied FIO2 in both protocols ( fig1. d ). The ' II-III ' phase act more distinct in differing two groups than ' I-II ' phase. And the low-high-low protocol acted almost perfect in accessing brain death with highest sensitivity and specificity. Over all, the novel incorporation of NIRS and a low-high-low varied FIO2 protocol was shown to a be most sensitive, highly specific, noninvasive and real time way to assess brain death and promptly offer quality assured donor organs. [1] E. F. M. Wijdicks, P. N. Varelas, G. S. Gronseth, D. M. Greer, Evidence-based guideline update: Determining brain death in adults report of the quality standards subcommittee of the American Academy of Neurology, Neurology, vol. 74, no. 23, pp. 1911-1918, 2010 [2] K. Singbartl, R. Murugan, A. M. Kaynar, D. W. Crippen, S. A. Tisherman, K. Shutterly, S. A. Stuart, R. Simmons, Intensivist-led management of brain-dead donors is associated with an increase in organ recovery for transplantation, J. M. Darby, Am. J. Transplant., vol. 11, no. 7, pp. 1517-1521, 2011 [3] T. Li, M. Duan, Y. Zhao, G. Yu, Z. Ruan. Bedside monitoring of patients with shock using a portable spatially-resolved near-infrared spectroscopy. Biomed. Opt. Express, vol. 6, no. 9, pp. 3431-3436, 2015 [4] B. Pan, C. Huang, X. Fang, X. Huang, T. Li*, Noninvasive and Sensitive Optical Assessment of Brain Death, J. Biophotonics, vol. 12, no. 3, pp. e201800240, 2018 Disclosures No relevant conflicts of interest to declare.

Spinal cord injury (SCI) is a high-cost disability and may cause permanent loss of movement and sensation below the injury location. The chance of cure in human after SCI is extremely limited. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. As an indicator of nonlinear dynamics in the brain, sample entropy was used here in combination with detrended fluctuation analysis (DFA) and Kolmogorov complexity to quantify functional plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the sample entropy values were decreased at the first day following injury then gradually increased during recovery. DFA and Kolmogorov complexity results were in consistent with sample entropy, showing the complexity of the EEG time series was lost after injury and partially regained in 1 week. The tendency to regain complexity is in line with the observation of behavioral rehabilitation. A critical time point was found during the recovery process after SCI. Our preliminary results suggested that the combined use of these nonlinear dynamical metrics could provide a quantitative and predictive way to assess the change of neural plasticity in a spinal cord injury rat model.

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and “aging” process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

Concise coding for input plays an important role in neural signal processing. In previous studies, three types of neural signal coding were raised: rate, timing and population coding. By multi-electrode array, the coding of input intensities in cultured hippocampal neuronal networks was examined. In excitatory neuronal network, responses to input intensities focused on the three periods: 1∼10 ms, 11∼30 ms, 41∼60 ms. The dynamics on firing rate to input intensities in each timings was distinct: in 1∼10 ms, the sublinear relationship to increasing input intensities was exhibited over the whole input range; in 11∼30 ms, only a transition was occurred in inputs from 300 to 400mV; in 41∼60 ms, a linear increase with input from 300 to 600 mV. These results suggest that the coding to input intensities was a complex mode that is repeat burst based-on timing in high density cultured neuronal network.

Slow oscillations in the hippocampus are correlated with memory consolidation and brain diseases. The characteristic firings of the hippocampal network in vitro are still poorly understood. Here, spontaneous oscillations (~0.004 Hz) were found in high-density hippocampal networks by multi-electrode arrays after 30 days in vitro. This kind of spontaneous activity was characterized by periodic synchronized superbursts, which persisted for approximately 60 s at long intervals. Additionally, 1-Hz stimulation (duration

Using multiple nonlinear techniques, we revealed the existence of chaos in the spontaneous activity of neuronal networks in vitro. The spatial-temporal dynamics of these networks indicated that emergent transition between chaotic behavior and superburst occurred periodically in low-frequency oscillations. An analysis of network-wide activity indicated that chaos was synchronized among different sites. Moreover, we found that the degree of chaos increased as the number of active sites in the network increased during long-term development (over three months in vitro). The chaotic behavior of the dissociated networks had similar spatial-temporal characteristics (rapid transition, periodicity, and synchronization) as the intact brain; however, the degree of chaos depended on the number of active sites at the mesoscopic level. This work could provide insight into neural coding and neurocybernetics.