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. © 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

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. © 2019, The Author(s).1

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. © 2020, The Author(s).1

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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.1

Cortical processing during robot and functional electrical stimulation

IntroductionLike 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.MethodsIn 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).ResultsIn 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.DiscussionOur 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

LSTM-CNN model of drowsiness detection from multiple consciousness states acquired by EEG

This study aimed to design a deep neural network for electroencephalography (EEG)-based drowsiness detection in multiple consciousness states, i.e., “awake,” “sleep,” and “drowsiness.” Few studies have seriously considered the optimal input vector size or labeling method in classifying multiple consciousness states, which may affect classification performance. To determine the optimal input vector length, i.e., window length, three neural network models (long short-term memory [LSTM], convolutional neural network [CNN], and combined LSTM and CNN) and four feature-based models were tested with six different levels of window length. The EEG dataset was acquired from 19 participants with randomly assigned auditory stimuli and button responses. The EEG data were labeled into three classes (awake, sleep, and drowsiness) based on the defined button response pattern corresponding to the stimuli. The results demonstrated that when the input vector size exceeded 8 sec, the performance of the neural network models dropped rapidly; however, when the window size was less than 8 sec, the performance change according to the window size was small. In contrast, the performance of feature-based models increased continuously as the window size increased. The LSTM model yielded the best accuracy (86%) for a 1 sec window length, and the LSTM-CNN model yielded the best kappa index (0.77) for a 4 sec window length. In addition, the proposed model was applied to the binary classification of normal consciousness (awake) and low consciousness (drowsiness and sleep) states to determine whether this model works appropriately in actual applications such as drowsiness detection in a driving environment. For binary classification, the LSTM-CNN model resulted in 0.95 F1 scores in 4000-ms. When a short input data (500 msec) is used, the LSTM-CNN model resulted in an average accuracy of 85.6% and a kappa index of 0.77 for the three-class classification problem and 0.94 F1 scores for the binary classification problem. In conclusion, we demonstrated that the proposed model could effectively detect drowsiness. Furthermore, a significant correlation was found between reaction time and drowsiness. However, using the reaction time as an index for labeling drowsiness was challenging because of the high false-negative ratio. © 2022 The Author(s)TRU

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. © 2011 Springer Science+Business Media, LLC.1

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. © 2012 IPEM.

Does FES Contribute to Cognitive Motor Task Discrimination?: An fNIRS study

FES, widely used in clinical rehabilitation, is a kind of involuntary motion stimulator to generate passive motion by stimulating the target muscles. However, it is not yet clear how FES affects cognitive performance of motor task. This study aims to investigate cerebral cortex activation of effect of minimal FES during voluntary cognitive motor task discrimination by using fNIRS. Seven healthy right-handed persons participated in the experiment. Participants voluntarily perform the flexion/extension of the right hand, alternately grasping DigiFlex, which have five different grip forces. At the same time, they answer the strength of DigiFlex's power, which is provided randomly each time, by a scale of 1 to 5. During the test, fNIRS measures the cerebral hemodynamics of all cortical areas and also is scored the answer rate. At this point, FES stimulates target muscles with a minimum current for the subject to grasp DigiFlex of the smallest intensity of 0.34kgf. The effect of cognitive motor task discrimination by a minimal FES was overall increased the correct answer rate, but the cortical motor facilitation was decreased. These conflicting results require more subjects' participation in the experiment and more rigorous statistical analysis in the future. Nevertheless, exploring the FES' contribution to cognitive motor task discrimination from the perspective of cortical facilitation could be a novel and interesting approach. © 2021 IEEE