| 1. |
- Bagheri, Zahra M., et al.
(författare)
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An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments
- 2017
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 14:4
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. Approach. We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets which move along long, continuous trajectories, as seen in our electrophysiological data. To test performance in real-world conditions, we implemented this model on a robotic platform that uses active pursuit strategies based on insect behaviour. Main results. Our robot performs robustly in closed-loop pursuit of targets, despite a range of challenging conditions used in our experiments; low contrast targets, heavily cluttered environments and the presence of distracters. We show that the facilitation stage boosts responses to targets moving along continuous trajectories, improving contrast sensitivity and detection of small moving targets against textured backgrounds. Moreover, the temporal properties of facilitation play a useful role in handling vibration of the robotic platform. We also show that the adoption of feed-forward models which predict the sensory consequences of self-movement can significantly improve target detection during saccadic movements. Significance. Our results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of our bio-inspired algorithm in an artificial visual system.
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| 2. |
- Lee, Heui Chang, et al.
(författare)
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Histological evaluation of flexible neural implants; Flexibility limit for reducing the tissue response?
- 2017
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Flexible neural probes are hypothesized to reduce the chronic foreign body response (FBR) mainly by reducing the strain-stress caused by an interplay between the tethered probe and the brain's micromotion. However, a large discrepancy of Young's modulus still exists (3-6 orders of magnitude) between the flexible probes and the brain tissue. This raises the question of whether we need to bridge this gap; would increasing the probe flexibility proportionally reduce the FBR? Approach. Using novel off-stoichiometry thiol-enes-epoxy (OSTE+) polymer probes developed in our previous work, we quantitatively evaluated the FBR to four types of probes with different softness: silicon (∼150 GPa), polyimide (1.5 GPa), OSTE+Hard (300 MPa), and OSTE+Soft (6 MPa). Main results. We observed a significant reduction in the fluorescence intensity of biomarkers for activated microglia/macrophages and blood-brain barrier (BBB) leakiness around the three soft polymer probes compared to the silicon probe, both at 4 weeks and 8 weeks post-implantation. However, we did not observe any consistent differences in the biomarkers among the polymer probes. Significance. The results suggest that the mechanical compliance of neural probes can mediate the degree of FBR, but its impact diminishes after a hypothetical threshold level. This infers that resolving the mechanical mismatch alone has a limited effect on improving the lifetime of neural implants.
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| 3. |
- Malesevic, Nebojsa, et al.
(författare)
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Exploration of sensations evoked during electrical stimulation of the median nerve at the wrist level
- 2023
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Ingår i: Journal of Neural Engineering. - 1741-2560 .- 1741-2552. ; 20:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Nerve rehabilitation following nerve injury or surgery at the wrist level is a lengthy process during which not only peripheral nerves regrow towards receptors and muscles, but also the brain undergoes plastic changes. As a result, at the time when nerves reach their targets, the brain might have already allocated some of the areas within the somatosensory cortex that originally processed hand signals to some other regions of the body. The aim of this study is to show that it is possible to evoke a variety of somatotopic sensations related to the hand while stimulating proximally to the injury, therefore, providing the brain with the relevant inputs from the hand regions affected by the nerve damage. Approach. This study included electrical stimulation of 28 able-bodied participants where an electrode that acted as a cathode was placed above the Median nerve at the wrist level. The parameters of electrical stimulation, amplitude, frequency, and pulse shape, were modulated within predefined ranges to evaluate their influence on the evoked sensations. Main results. Using this methodology, the participants reported a wide variety of somatotopic sensations from the hand regions distal to the stimulation electrode. Significance. Furthermore, to propose an accelerated stimulation tuning procedure that could be implemented in a clinical protocol and/or standalone device for providing meaningful sensations to the somatosensory cortex during nerve regeneration, we trained machine-learning techniques using the gathered data to predict the location/area, naturalness, and sensation type of the evoked sensations following different stimulation patterns.
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| 4. |
- Rohlén, Robin, et al.
(författare)
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A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics
- 2023
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Ingår i: Journal of Neural Engineering. - 1741-2560 .- 1741-2552.
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g., the active MU displacements, arterial pulsations, bones, connective tissue, and noise.Approach: This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e., the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings.Main results: We found that the computational time for velBSS was at least 20 times less than for stICA, while the twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13).Significance: The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.
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| 5. |
- Rohlén, Robin, et al.
(författare)
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Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions
- 2023
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Ingår i: Journal of Neural Engineering. - : Institute of Physics (IOP). - 1741-2560 .- 1741-2552. ; 20:4, s. 046016-
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Ultrafast ultrasound (UUS) imaging has been used to detect intramuscular mechanical dynamics associated with single motor units (MUs). Detecting MUs from ultrasound sequences requires decomposing a velocity field into components, each consisting of an image and a signal. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the signals with MU firings obtained from needle electromyography (EMG). Here, we examined whether the repeatability of the images over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions.Approach: UUS images and high-density surface EMG (HDsEMG) were recorded simultaneously from 99 MUs in the biceps brachii of five healthy subjects. The MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based components. For each contraction, velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. To evaluate the repeatability of components' images across epochs, we calculated the Jaccard Similarity Coefficient (JSC). JSC compares the similarity between two images providing values between 0 and 1. Finally, the association between the components and the MUs from HDsEMG was assessed.Main results: All the MU-matched components had JSC>0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8±1.6 matches over 4.9±1.8 MUs). The repeatable components (JSC>0.38) represented 14% of the total components (6.5±3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from noise and can be used for data reduction.Significance: This study provides the foundation for developing stand-alone methods to identify MU in UUS sequences and towards real-time imaging of MUs. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.
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| 6. |
- Smaragdos, G., et al.
(författare)
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BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations
- 2017
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 14:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the Inferior-Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. Main results: The combined use of different HPC fabrics demonstrated that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments. Our performance analysis shows clearly that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance: The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.
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| 7. |
- Wilroth, Johanna, et al.
(författare)
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Improving EEG-based decoding of the locus of auditory attention through domain adaptation
- 2023
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Ingår i: Journal of Neural Engineering. - : Institute of Physics (IOP). - 1741-2560 .- 1741-2552. ; 20:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices. © 2023 The Author(s). Published by IOP Publishing Ltd.
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| 8. |
- Löfhede, Johan, et al.
(författare)
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Classification of burst and suppression in the neonatal electroencephalogram
- 2008
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Ingår i: Journal of Neural Engineering. - : Institute of Physics Publishing Ltd.. - 1741-2560 .- 1741-2552. ; 5:4, s. 402-410
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Tidskriftsartikel (refereegranskat)abstract
- Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern. Five features extracted from the EEG were used as inputs. The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods. Based on this, the SVM performs slightly better than the others. Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods.
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| 9. |
- Löfhede, Johan, 1978, et al.
(författare)
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Automatic classification of background EEG activity in healthy and sick neonates
- 2010
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher’s linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.
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| 10. |
- Mohlin, Camilla, 1972-, et al.
(författare)
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Further assessment of neuropathology in retinal explants and neuroprotection by human neural progenitor cells
- 2011
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 8:6, s. 066012-
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Tidskriftsartikel (refereegranskat)abstract
- Explanted rat retinas show progressive photoreceptor degeneration that appears to be caspase-12-dependent. Decrease in photoreceptor density eventually affects the inner retina, particularly in the bipolar cell population. Explantation and the induced photoreceptor degeneration are accompanied by activation of Muller and microglia cells. The goal of this study was to determine whether the presence of a feeder layer of human neural progenitor cells (hNPCs) could suppress the degenerative and reactive changes in the explants. Immunohistochemical analyses showed considerable sprouting of rod photoreceptor axon terminals into the inner retina and reduced densities of cone and rod bipolar cells. Both sprouting and bipolar cell degenerations were significantly lower in retinas cultured with feeder layer cells compared to cultured controls. A tendency toward reduced microglia activation in the retinal layers was also noted in the presence of feeder layer cells. These results indicate that hNPCs or factors produced by them can limit the loss of photoreceptors and secondary injuries in the inner retina. The latter may be a consequence of disrupted synaptic arrangement.
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