Low Back Pain Assessment Based on Alpha Oscillation Changes in Spontaneous Electroencephalogram (EEG).

Objectively and accurately assessing pain in clinical settings is challenging. Previous studies showed that alpha oscillations of electroencephalogram data are correlated with subjective perceived pain. Based on this finding, this study is aimed at assessing chronic low back pain based on alpha oscillations. Multichannel electroencephalogram data were recorded from 27 subjects with chronic low back pain under the simple conditions of closing eyes or opening eyes. Spectral analyses were conducted to extract the alpha band responses, and the alpha powers were calculated for the two conditions, respectively. Normalized alpha power was calculated by subtracting the alpha power in the eyes-open condition from that in the eyes-closed condition. The correlation between the alpha power and the subjective pain intensity was evaluated in frontal, central, and posterior regions. The normalized alpha power in the central region was negatively correlated with the subjective pain intensity (R = -0.50, P = 0.01), with the strongest correlation occurring at the Cz electrode (R = -0.59, P = 0.04). The correlation analysis results demonstrated the possibility of using the differences of alpha spectral power between eyes-closed and eyes-open conditions as a measure for assessing chronic low back pain. The findings suggest that the normalized alpha power in the central region may be used as a measurable and quantitative indicator of chronic pain for clinical applications.

[Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials].

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Electrochemical characteristics of microelectrode designed for electrical stimulation.

BACKGROUND: Microelectrode arrays play an important role in prosthetic implants for neural signal recording or applying electrical pulses stimulation to target nerve system. Safety and long-term reliability are essential requirements for microelectrode arrays applied in electrical stimulation. In design and fabrication of the microelectrode array, soft materials are generally chosen to be the substrate for the aim of achieving better compliance with the surrounding tissue while maintaining minimal damage. By flexing of the array to the surface, the array is capable of keeping a more stable electrical contact resulting in a significantly improved signal detected. METHODS: In this study, we design and fabricate a flexible microelectrode array with gold as the electrode material and parylene-C as the substrate. The fabrication process of the array is presented. The in vitro electrochemical characteristics of the microelectrode are investigated by electrochemical impedance spectroscopy and cyclic voltammetry in a three-electrode electrochemical cell containing phosphate-buffered saline. Charge injection capacity measurements are carried out by multichannel systems and the CSC of the microarray is calculated. RESULTS: Electrochemical results showed that impedance decreased with frequency. The average impedance of the Au electrodes at 1 kHz was 36.54 ± 0.88 kΩ. The average phase angle at 1 kHz was - 73.52 ± 1.3°, and the CIC of the microelectrode was 22.3 µC/cm2. The results demonstrated that the microelectrode array performed as expected for neuronal signal recording or stimulation. CONCLUSIONS: With parylene-C as the substrate, the microarray has good flexibility. The electrochemical characteristics' results show that the array has the ability to resist any corrosion on metal-electrolyte interface and has good biocompatibility. This low-cost, flexible parylene-based, gold microelectrode array shows potential for use in implant neurological signal acquisition or neurostimulation applications.

Effects of stimulation frequency and stimulation waveform on steady-state visual evoked potentials using a computer monitor.

OBJECTIVE: A visual stimulator plays a vital part in brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP). The properties of visual stimulation, such as frequency, color, and waveform, will influence SSVEP-based BCI performance to some extent. Recently, the computer monitor serves as a visual stimulator that is widespread in SSVEP-based BCIs because of its great flexibility in generating visual stimuli. However, stimulation properties based on a computer monitor have received very little attention. For a better comprehension of SSVEPs, this study explored the stimulation effects of waveforms and frequencies, when evoking SSVEPs through a computer monitor. APPROACH: This study utilized the approximation methods to realize sine- and square-wave temporal modulations at 18 stimulation frequencies ranging from 6 to 40 Hz on a conventional 120 Hz LCD screen. We collected electroencephalogram (EEG) datasets from 12 healthy subjects and compared the signal-to-noise ratios (SNRs), amplitudes, and topographic mapping of SSVEPs evoked by these two temporal modulation flickers (sine- and square-wave). In addition, a BCI experiment with two nine-target BCIs (i.e. low-frequency BCI and high-frequency BCI) was implemented to compare the two stimulation waveforms in terms of BCI performance. MAIN RESULTS: For both sine- and square-wave stimulation conditions, strong SSVEPs over the occipital area were observed for each stimulation frequency. SSVEP amplitudes at the stimulation frequency exhibited a global peak in the low-frequency band. The second harmonic SSVEP frequency-response functions showed the largest amplitude at 6 Hz and fell sharply for higher frequencies. In the BCI experiment, the classification performance of the square-wave stimuli was notably higher than that of the sine-wave stimuli when using shorter data lengths. SIGNIFICANCE: These results suggested that the square-wave flicker was more efficient at implementing high-speed BCIs based on SSVEP when using a computer monitor as a visual stimulator.

Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning.

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets. We then realize automatic image segmentation with deep learning by using convolutional neural network. We also introduce parallel computing. Such approaches greatly reduced the processing time compared to manual and semiautomatic segmentation and are of great importance in improving the speed and accuracy as more and more samples are being learned. The segmented data of grey and white matter are counted by computer in volume, which indicates the potential of this segmentation technology in diagnosing cerebral atrophy quantitatively. We demonstrate the great potential of such image processing and deep learning-combined automatic tissue image segmentation in neurology medicine.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials.

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.

Trial-to-trial latency variability of somatosensory evoked potentials as a prognostic indicator for surgical management of cervical spondylotic myelopathy.

BACKGROUND: Early detection of neural conductivity changes at the compressed spinal cord is important for predicting the surgical outcomes of patients with cervical spondylotic myelopathy (CSM). The prognostic value of median nerve somatosensory evoked potential (SEP) has been proposed previously. The present prospective study evaluates the use of trial-to-trial variability in SEP as a valuable predictor of neurological recovery after surgery of CSM. METHODS: A total of 35 CSM patients who underwent surgery with up to 6-month follow-up were recruited in this study. SEP signals were recorded preoperatively. The single trial SEP was extracted by a newly developed second-order blind identification method. The postoperative recovery was assessed using the modified Japanese Orthopaedic Association. The correlation between the latency variability of trial-to-trial SEP and post-operative recovery ratio was analyzed. The prognostic value of trial-to-trial SEP for CSM was evaluated using a receiver operator characteristic curve which can accurately reflect the relationship between sensitivity and specificity of a diagnostic method and represent the accuracy of prognosis. RESULTS: The correlation coefficient of trial-to-trial latency variability and the 6-month recovery ratio was statistically significant (r = -0.82, P < 0.01). The trial-to-trial SEP had a higher prognostic accuracy (AUC = 0.928, P < 0.001) with an optimal prognostic value of 9.25 % compared with averaged SEP when the threshold of recovery ratio was 40 %, and was more sensitive (93.80 %) than the averaged SEP (43.80 %). CONCLUSIONS: These findings indicate that the latency variability of trial-to-trial SEP reflect the recovery ratio of CSM patients after surgery. It is suggested that the latency variability of trial-to-trial SEP is useful for predicting the surgical outcomes for patients with CSM, which would be a potential indication of surgical treatment for CSM to help decision making of surgical planning for CSM patients.

Field distribution of epidural electrical stimulation.

Epidural electrical stimulation has been applied in clinics for many years. However, there is still a concern about possible injury to spinal nerves. This study investigated electrical field and current density distribution during direct epidural electrical stimulation. Field distribution models were theoretically deduced, while the distribution of potentials and current were analyzed. The current density presented an increase of 70-80%, with one peak value ranging from -85° to 85° between the two stimulated poles. The effect of direct epidural electrical stimulation is mainly on local tissue surrounding the electrodes, concentrated around the two stimulated positions.