Pain Classification using Evoked EEG Induced by Thermal Grill Illusion - Deep Neural Network Approach.

As the quantification of pain has emerged in biomedical engineering today, studies have been developing biomarkers associated with pain actively by measuring bio-signals such as electroencephalogram (EEG). Recently, some EEG studies of cold and hot pain have been reported. However, they used one type of stimulus condition for each trial and a relatively long stimulation time to collect EEG features. In this study, EEG signals during Cool (20 °C), Warm (40 °C), and Thermal Grill Illusion (TGI, 20-40 °C) stimuli were collected from 43 subjects, and were classified by a deep convolutional neural network referred to as EEGNet. Three binary classifications for the three conditions (TGI, Cool, Warm) were conducted for each subject individually. Classification accuracies for TGI-Cool, TGI-Warm, and Warm-Cool were 0.74±0.01, 0.71±0.01, and 0.74±0.01, respectively. For subjects who rated the TGI significantly hotter than the Warm stimulus, the classification accuracy for TGI-Cool (0.74±0.01) was significantly higher than for TGI-Warm (0.71±0.01). In contrast, the classification accuracy for TGI-Cool (0.72±0.03) did not differ statistically from TGI-Warm (0.73±0.01) in subjects without illusion. We found that the TGI and Cool stimuli were classified better than the TGI and Warm stimuli, implying that objective EEG features are consistent with subjective behavioral results. Further, we observed that most discriminative features between the TGI and the Cool or Warm conditions appeared in the parietal area for subjects who perceived the illusion. We postulate that the somato-sensory cortex may be activated when TGI is perceived to be hot pain.

Cortical processing during robot and functional electrical stimulation.

Introduction: Like alpha rhythm, the somatosensory mu rhythm is suppressed in the presence of somatosensory inputs by implying cortical excitation. Sensorimotor rhythm (SMR) can be classified into two oscillatory frequency components: mu rhythm (8-13 Hz) and beta rhythm (14-25 Hz). The suppressed/enhanced SMR is a neural correlate of cortical activation related to efferent and afferent movement information. Therefore, it would be necessary to understand cortical information processing in diverse movement situations for clinical applications. Methods: In this work, the EEG of 10 healthy volunteers was recorded while fingers were moved passively under different kinetic and kinematic conditions for proprioceptive stimulation. For the kinetics aspect, afferent brain activity (no simultaneous volition) was compared under two conditions of finger extension: (1) generated by an orthosis and (2) generated by the orthosis simultaneously combined and assisted with functional electrical stimulation (FES) applied at the forearm muscles related to finger extension. For the kinematic aspect, the finger extension was divided into two phases: (1) dynamic extension and (2) static extension (holding the extended position). Results: In the kinematic aspect, both mu and beta rhythms were more suppressed during a dynamic than a static condition. However, only the mu rhythm showed a significant difference between kinetic conditions (with and without FES) affected by attention to proprioception after transitioning from dynamic to static state, but the beta rhythm was not. Discussion: Our results indicate that mu rhythm was influenced considerably by muscle kinetics during finger movement produced by external devices, which has relevant implications for the design of neuromodulation and neurorehabilitation interventions.

Effectiveness of a Mobile Wellness Program for Nurses with Rotating Shifts during COVID-19 Pandemic: A Pilot Cluster-Randomized Trial.

Nurses with rotating shifts, including night shifts, have suffered from low physical activity during the COVID-19 pandemic and lower sleep quality due to the disruption of their circadian rhythm. This study aimed to develop and examine the effectiveness of a mobile wellness program on daily steps, sleep quality, exercise self-efficacy, intrinsic motivation for exercise, self-rated fatigue, and wellness. A cluster randomized controlled trial design was used to examine the effectiveness of the mobile wellness program for nurses with rotating shifts. Sixty nurses from one university hospital participated and were allocated to an intervention group and a control group. The intervention group received a 12-week mobile wellness program to improve their physical activity and sleep quality, and the control group was only given a Fitbit to self-monitor their health behaviors. There were significant differences between the two groups in daily steps (p = 0.000), three components (subjective sleep quality, sleep disturbance, daytime dysfunction) of the PSQI, exercise self-efficacy, intrinsic motivation for exercise, and wellness. In conclusion, this study provides meaningful information that the mobile wellness program using Fitbit, online exercise using Zoom, online health coaching on a Korean mobile platform, and motivational text messages effectively promoted physical activity and sleep quality for nurses with rotating shifts during the COVID-19 pandemic.

Gait Disturbance Improvement and Cerebral Cortex Rearrangement by Acupuncture in Parkinson's Disease: A Pilot Assessor-Blinded, Randomized, Controlled, Parallel-Group Trial.

BACKGROUND: Parkinson's disease (PD) leads to impaired mobility and limited independence. OBJECTIVE: We investigated the effects of acupuncture on gait disturbance and analyzed hemodynamic changes caused by acupuncture in the cerebral cortex of patients with PD. METHODS: Participants (n = 26) with gait disturbance due to PD were randomly assigned to the intervention (acupuncture twice a week for 4 weeks + conventional therapy) or control (conventional therapy) groups. We analyzed gait parameters using the GAITRite system and hemodynamic responses in the cerebral cortices using functional near-infrared spectroscopy, Unified Parkinson's Disease Rating Scale (UPDRS) scores, neurotransmitter levels, as well as the immediate effects of acupuncture in patients with PD. RESULTS: The participants tended to walk with hypometric gait (high cadence, short steps) overground. After acupuncture treatment, those in the intervention group showed a significant reduction in cadence and the UPDRS scores involving "walking and balance" compared with those in the control group (P = .004 and P = .020, respectively); the stride, swing, and single support times were significantly increased (P = .006, P = .001, and P = .001, respectively). Oxyhemoglobin levels in the intervention group while walking on a treadmill were significantly increased in the prefrontal and supplementary motor areas. The oxyhemoglobin levels in the prefrontal cortex and swing time revealed significant positive correlations. CONCLUSIONS: Our findings indicated that acupuncture tended to improve hypometric gait and rearranged activation of the cerebral cortex. Thus, acupuncture may be a useful complementary treatment for gait disturbance, including hypometric gait, in patients with PD. Trial Registration Number. Clinical Research Information Service (KCT0002603), https://cris.nih.go.kr/cris/index.jsp.

Hemispheric asymmetry in hand preference of right-handers for passive vibrotactile perception: an fNIRS study.

Hemispheric asymmetry in hand preference for passive cutaneous perception compared to active haptic perception is not well known. A functional near-infrared spectroscopy was used to evaluate the laterality of cortical facilitation when 31 normal right-handed participants were involved in 205 Hz passive vibrotactile cutaneous stimuli on their index fingers of preferred and less-preferred hand. Passive cutaneous perception resulted that preferred (right) hand stimulation was strongly leftward lateralized, whereas less-preferred (left) hand stimulation was less lateralized. This confirms that other manual haptic exploration studies described a higher hemispheric asymmetry in right-handers. Stronger cortical facilitation was found in the right primary somatosensory cortex (S1) and right somatosensory association area (SA) during left-hand stimulation but not right-hand stimulation. This finding suggests that the asymmetric activation in the S1 and SA for less-preferred (left) hand stimulation might contribute to considerably reinforce sensorimotor network just with passive vibrotactile cutaneous stimulation.

The difference in cortical activation pattern for complex motor skills: A functional near- infrared spectroscopy study.

The human brain is lateralized to dominant or non-dominant hemispheres, and controlled through large-scale neural networks between correlated cortical regions. Recently, many neuroimaging studies have been conducted to examine the origin of brain lateralization, but this is still unclear. In this study, we examined the differences in brain activation in subjects according to dominant and non-dominant hands while using chopsticks. Fifteen healthy right-handed subjects were recruited to perform tasks which included transferring almonds using stainless steel chopsticks. Functional near-infrared spectroscopy (fNIRS) was used to acquire the hemodynamic response over the primary sensory-motor cortex (SM1), premotor area (PMC), supplementary motor area (SMA), and frontal cortex. We measured the concentrations of oxy-hemoglobin and deoxy-hemoglobin induced during the use of chopsticks with dominant and non-dominant hands. While using the dominant hand, brain activation was observed on the contralateral side. While using the non-dominant hand, brain activation was observed on the ipsilateral side as well as the contralateral side. These results demonstrate dominance and functional asymmetry of the cerebral hemisphere.

Distinction of directional coupling in sensorimotor networks between active and passive finger movements using fNIRS.

The purpose of this study is to investigate cerebral cortex activation during active movement and passive movement by using a functional near-infrared spectroscopy (fNIRS). Tasks were the flexion/extension of the right hand finger by active movement and passive movement. Oxy-hemoglobin concentration changes calculated from fNIRS and analyzed the activation and connectivity so as to understand dynamical brain relationship. The results demonstrated that the brain activation in passive movements is similar to motor execution. During active movement, the estimated causality patterns showed significant causality value from the supplementary motor area (SMA) to the primary motor cortex (M1). During the passive movement, the causality from the primary somatosensory cortex (S1) to the primary motor cortex (M1) was stronger than active movement. These results demonstrated that active and passive movements had a direct effect on the cerebral cortex but the stimulus pathway of active and passive movement is different. This study may contribute to better understanding how active and passive movements can be expressed into cortical activation by means of fNIRS.

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network.

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: an fNIRS pilot study.

Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.

Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning.

Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.

An adaptive Cauchy differential evolution algorithm for global numerical optimization.

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

An approach to self-assembling swarm robots using multitree genetic programming.

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms.

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.

Decision support algorithm for diagnosis of ADHD using electroencephalograms.

Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach provides higher accuracy in diagnostic process of ADHD disorder than the few currently available methods.