Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness.

Objectives: To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. Participants and methods: The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and ß (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. Results: The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher ß band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (ß band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. Conclusion: The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.

Objective: The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Methods: Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. Results: PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Conclusion: Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.

The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.

A transfer learning framework based on motor imagery rehabilitation for stroke.

Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced 'fine-tune' to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and 'fine-tune' transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

Effects of ultrafine grinding on physicochemical, functional and surface properties of ginger stem powders.

BACKGROUND: Ginger stem (GS) is a by-product of ginger processing. It is not directly edible as a feed or food, which leads to it being discarded as waste or burned. Accordingly, it is very important to develop new functional products in the food or feed industry as a result of high nutritional and medicinal values. In the present study, the structures and physicochemical properties of GS powders of different sizes were evaluated after ultrafine grinding by a vibrating mill. RESULTS: The ultrafine powders exhibited a smaller particle size and uniform distribution. Higher values in bulk density (from 1.07 ± 0.06 to 1.62 ± 0.08 g mL-1 ), oil holding capacity (from 3.427 ± 0.04 to 4.83 ± 0.03 g mL-1 ), and repose and slide angles (from 42.33 ± 1.52 to 54.36 ± 1.15° and 33.62 ± 0.75 to 47.27 ± 1.34°, respectively) of ultrafine GS powders were exhibited compared to coarse powders. With a reduced particle size, the solubility of ultrafine powders increased significantly (P < 0.05), whereas the water holding and swelling capacities decreased with a reduced particle size and then increased. Fourier transform infrared spectroscopy analysis showed that ultrafine grinding did not damage the main cellular structure of GS powder. The reduction of fiber length and particle size in GS was observed by light microscopy and scanning electron microscopy. The X-ray diffraction patterns demonstrated the crystallinity and the intensity of the peak in superfine GS powders. CONCLUSION: The present study suggests that ultrafine grinding treatments influence the structures and physicochemical properties of GS powders, and such changes would improve the effective utilization of GS in the food or feed industry. © 2020 Society of Chemical Industry.