Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic Stroke Patients: A Fuzzy Approximate Entropy Analysis

Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice

Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.

Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke

Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.

Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability

BACKGROUND

Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures.

METHODS

This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported.

RESULTS

A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population.

CONCLUSION

Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.

Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke

Background

We investigated the effects of brain-computer interface (BCI) combined with mental practice (MP) and occupational therapy (OT) on performance in activities of daily living (ADL) in stroke survivors.

Methods

Participants were randomized into two groups: experimental (n = 23, BCI controlling a hand exoskeleton combined with MP and OT) and control (n = 21, OT). Subjects were assessed with the functional independence measure (FIM), motor activity log (MAL), amount of use (MAL-AOM), and quality of movement (MAL-QOM). The box and blocks test (BBT) and the Jebsen hand functional test (JHFT) were used for the primary outcome of performance in ADL, while the Fugl-Meyer Assessment was used for the secondary outcome. Exoskeleton activation and the degree of motor imagery (measured as event-related desynchronization) were assessed in the experimental group. For the BCI, the EEG electrodes were placed on the regions of FC3, C3, CP3, FC4, C4, and CP4, according to the international 10-20 EEG system. The exoskeleton was placed on the affected hand. MP was based on functional tasks. OT consisted of ADL training, muscle mobilization, reaching tasks, manipulation and prehension, mirror therapy, and high-frequency therapeutic vibration. The protocol lasted 1 h, five times a week, for 2 weeks.

Results

There was a difference between baseline and post-intervention analysis for the experimental group in all evaluations: FIM (p = 0.001, d = 0.56), MAL-AOM (p = 0.001, d = 0.83), MAL-QOM (p = 0.006, d = 0.84), BBT (p = 0.004, d = 0.40), and JHFT (p = 0.001, d = 0.45). Within the experimental group, post-intervention improvements were detected in the degree of motor imagery (p < 0.001) and the amount of exoskeleton activations (p < 0.001). For the control group, differences were detected for MAL-AOM (p = 0.001, d = 0.72), MAL-QOM (p = 0.013, d = 0.50), and BBT (p = 0.005, d = 0.23). Notably, the effect sizes were larger for the experimental group. No differences were detected between groups at post-intervention.

Conclusion

BCI combined with MP and OT is a promising tool for promoting sensorimotor recovery of the upper limb and functional independence in subacute post-stroke survivors.

Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain

Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.

EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training

Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI–FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl–Meyer assessment scale (FMA) score was significantly improved in the BCI–FES group (p < 0.05), and there was no significant difference in the FES group. (ii) Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. (iii) The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention (p < 0.05), and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group (p < 0.05). These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training.

The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance

Motor imagery-based brain-computer interface (MI-BCI) has been largely studied to improve motor learning and promote motor recovery. However, the difficulty in performing MI limits the widespread application of MI-BCI. It has been suggested that the usage of sensory threshold somatosensory electrical stimulation (st-SES) is a promising way to guide participants on MI tasks, but it is still unclear whether st-SES is effective for all users. In the present study, we aimed to examine the effects of st-SES on the MI-BCI performance in two BCI groups (High Performers and Low Performers). Twenty healthy participants were recruited to perform MI and resting tasks with EEG recordings. These tasks were modulated with or without st-SES. We demonstrated that st-SES improved the performance of MI-BCI in the Low Performers, but led to a decrease in the accuracy of MI-BCI in the High Performers. Furthermore, for the Low Performers, the combination of st-SES and MI resulted in significantly greater event-related desynchronization (ERD) and sample entropy of sensorimotor rhythm than MI alone. However, the ERD and sample entropy values of MI did not change significantly during the st-SES intervention in the High Performers. Moreover, we found that st-SES had an effect on the functional connectivity of the fronto-parietal network in the alpha band of Low Performers and the beta band of High Performers, respectively. Our results demonstrated that somatosensory input based on st-SES was only beneficial for sensorimotor cortical activation and MI-BCI performance in the Low Performers, but not in the High Performers. These findings help to optimize guidance strategies to adapt to different categories of users in the practical application of MI-BCI.

Brain–Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study

Introduction

We evaluated the efficacy of brain–computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.

Methods

In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.

Results

Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P &lt; 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P &lt; 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P &lt; 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P &lt; 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P &lt; 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P &lt; 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P &lt; 0.05).

Conclusion

The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.

Clinical Trial Registration

www.chictr.org.cn, identifier: ChiCTR2100046301.

Functional brain networks assessed with surface electroencephalography for predicting motor recovery in a neural guided intervention for chronic stroke

Predicting whether a chronic stroke patient is likely to benefit from a specific intervention can help patients establish reasonable expectations. It also provides the basis for candidates selecting for the intervention. Recent convergent evidence supports the value of network-based approach for understanding the relationship between dysfunctional neural activity and motor deficits after stroke. In this study, we applied resting-state brain connectivity networks to investigate intervention-specific predictive biomarkers of motor improvement in 22 chronic stroke participants who received either combined action observation with EEG-guided robot-hand training (Neural Guided-Action Observation Group, n = 12, age: 34–68 years) or robot-hand training without action observation and EEG guidance (non-Neural Guided-text group, n = 10, age: 42–57 years). The robot hand in Neural Guided-Action Observation training was activated only when significant mu suppression (8–12 Hz) was detected from participant’s EEG signals in ipsilesional hemisphere while it was randomly activated in non-Neural Guided-text training. Only the Neural Guided-Action Observation group showed a significant long-term improvement in their upper-limb motor functions (P < 0.5). In contrast, no significant training effect on the paretic motor functions was found in the non-Neural Guided-text group (P > 0.5). The results of brain connectivity estimated via EEG coherence showed that the pre-training interhemispheric connectivity of delta, theta, alpha and contralesional connectivity of beta were motor improvement related in the Neural Guided-Action Observation group. They can not only differentiate participants with good and poor recovery (interhemispheric delta: P = 0.047, Hedges’ g = 1.409; interhemispheric theta: P = 0.046, Hedges’ g = 1.333; interhemispheric alpha: P = 0.038, Hedges’ g = 1.536; contralesional beta: P = 0.027, Hedges’ g = 1.613) but also significantly correlated with post-training intervention gains (interhemispheric delta: r = −0.901, P < 0.05; interhemispheric theta: r = −0.702, P < 0.05; interhemispheric alpha: r = −0.641, P < 0.05; contralesional beta: r = −0.729, P < 0.05). In contrast, no EEG coherence was significantly correlated with intervention gains in the non-Neural Guided-text group (all Ps>0.05). Partial least square regression showed that the combination of pre-training interhemispheric and contralesional local connectivity could precisely predict intervention gains in the Neural Guided-Action Observation group with a strong correlation between predicted and observed intervention gains (r = 0.82r=0.82) and between predicted and observed intervention outcomes (r = 0.90r=0.90). In summary, EEG-based resting-state brain connectivity networks may serve clinical decision-making by offering an approach to predicting Neural Guided-Action Observation training-induced motor improvement.

Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy

The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.

Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study

Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8–12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement.

Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].

Feature Selection Using Extreme Gradient Boosting Bayesian Optimization to upgrade the Classification Performance of Motor Imagery signals for BCI

BACKGROUND

A motor imagery (MI) based brain computer interface (BCI) is a challenging nonmuscular connection system used to independently perform movement-related tasks. It is gaining increasing prominence in helping paralyzed individuals communicate with the real world. Achieving better classification accuracy is the major concern in the field of motor imagery-based BCI. To upgrade the classification performance, relevant features play a vital role. The relevant features can be selected by the extreme gradient Bayesian optimization (XGBO) method.

METHODS

In this paper, a combination of time-, frequency-, and spatial-related MI features are extracted to design a reliable MI-BCI system. The proposed method incorporates the XGBO algorithm for feature selection and the random forest for the classification of EEG signals. The potency of the proposed system was investigated using two public EEG datasets (BCI Competition III dataset IIIa and dataset IVa). A novel XGBO algorithm increases the accuracy and reduces the time consumption by reducing the dimensionality of features. The proposed algorithm selects the minimum number of features that increase the computational efficacy for MI-based BCI systems.

COMPARISON WITH EXISTING METHODS

The proposed method is compared with ANOVA, sequential forward selection, recursive feature elimination, and LASSO methods and the accuracy rate is increased with the lowest computation time.

RESULTS

The proposed method achieves mean accuracies of 94.44% and 88.72% and classification errors of 5.56% and 11.28% for Datasets IIIa and IVa, respectively. It outperforms four state-of-art methods with 0.87% and 0.59% increases in the accuracy for Datasets IIIa and IVa, respectively.

Fuzzy Approximate Entropy of Extrema Based on Multiple Moving Averages as a Novel Approach in Obstructive Sleep Apnea Screening

Objective: Obstructive sleep apnea (OSA) is a respiratory disease associated with autonomic nervous system dysfunction. As a novel method for analyzing OSA depending on heart rate variability, fuzzy approximate entropy of extrema based on multiple moving averages (Emma-fApEn) can effectively assess the sympathetic tension limits, thereby realizing a good performance in the disease severity screening. Method: Sixty 6-h electrocardiogram recordings (20 healthy, 16 mild/moderate OSA and 34 severe OSA) from the PhysioNet database were used in this study. The performances of minima of Emma-fApEn (fApEn-minima), maxima of Emma-fApEn (fApEn-maxima) and classic time-frequency domain indices for each recording were assessed by significance analysis, correlation analysis, parameter optimization and OSA screening. Results: fApEn-minima and fApEn-maxima had significant differences between the severe OSA group and the other two groups, while the mean value (Mean) and the ratio of low-frequency power and high-frequency power (LH) could significantly differentiate OSA recordings from healthy recordings. The correlation coefficient between fApEn-minima and apnea-hypopnea index was the highest (|R| = 0.705). Machine learning methods were used to evaluate the performances of the above four indices. Random forest (RF) achieved the highest accuracy of 96.67% in OSA screening and 91.67% in severe OSA screening, with a good balance in both. Conclusion: Emma-fApEn may be used as a simple preliminary detection tool to assess the severity of OSA prior to polysomnography analysis.

Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

Abnormal EEG Complexity and Alpha Oscillation of Resting State in Chronic Stroke Patients

A valid evaluation of neurological functions after stroke may improve clinical decision-making. The aim of this study was to compare the performance of EEG-related indexes in differentiating stroke patients from control participants, and to investigate pathological EEG changes after chronic stroke. 20 stroke and 13 healthy participants were recruited, and spontaneous EEG signals were recorded during the resting state. EEG rhythms and complexity were calculated based on Fast Fourier Transform and the fuzzy approximate entropy (fApEn) algorithm. The results showed a significant difference of alpha rhythm (p = 0.019) and fApEn (p = 0.003) of EEG signals from brain area among ipsilesional, contralesion hemisphere of stroke patients and corresponding brain hemisphere of healthy participants. EEG fApEn had the best classification accuracy (84.85%), sensitivity (85.00%), and specificity (84.62%) among these EEG-related indexes. Our study provides a potential method to evaluate alterations in the properties of the injured brain, which help us to understand neurological change in chronic strokes.

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Entropy modulation of electroencephalographic signals in physiological aging

Aging is a multifactorial physiological process characterized by the accumulation of degenerative processes impacting on different brain functions, including the cognitive one. A tool largely employed in the investigation of brain networks is the electroencephalogram (EEG). Given the cerebral complexity and dynamism, many non-linear approaches have been applied to explore age-related brain electrical activity modulation detected by the EEG: one of them is the entropy, which measures the disorder of a system. The present study had the aim to investigate aging influence on brain dynamics applying Approximate Entropy (ApEn) parameter to resting state EEG data of 68 healthy adult participants, divided with respect to their age in two groups, focusing on several specialized brain regions. Results showed that elderly participants present higher ApEn values than younger participants in the central, parietal and occipital areas, confirming the hypothesis that aging is characterized by an evolution of brain dynamics. Such findings may reflect a reduced synchronization of the neural networks cyclic activity, due to the reduction of cerebral connections typically found in aging process. Understanding the dynamics of brain networks by applying the entropy parameter could be useful for developing appropriate and personalized rehabilitation programs and for future studies on neurodegenerative diseases.

Influence of iTBS on the Acute Neuroplastic Change After BCI Training

Objective: Brain-computer interface (BCI) training is becoming increasingly popular in neurorehabilitation. However, around one third subjects have difficulties in controlling BCI devices effectively, which limits the application of BCI training. Furthermore, the effectiveness of BCI training is not satisfactory in stroke rehabilitation. Intermittent theta burst stimulation (iTBS) is a powerful neural modulatory approach with strong facilitatory effects. Here, we investigated whether iTBS would improve BCI accuracy and boost the neuroplastic changes induced by BCI training. Methods: Eight right-handed healthy subjects (four males, age: 20-24) participated in this two-session study (BCI-only session and iTBS+BCI session in random order). Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) and single-pulse transcranial magnetic stimulation (TMS). In BCI-only session, fNIRS was measured at baseline and immediately after BCI training. In iTBS+BCI session, BCI training was followed by iTBS delivered on the right primary motor cortex (M1). Single-pulse TMS was measured at baseline and immediately after iTBS. fNIRS was measured at baseline, immediately after iTBS, and immediately after BCI training. Paired-sample t-tests were used to compare amplitudes of motor-evoked potentials, cortical silent period duration, oxygenated hemoglobin (HbO2) concentration and functional connectivity across time points, and BCI accuracy between sessions. Results: No significant difference in BCI accuracy was detected between sessions (p > 0.05). In BCI-only session, functional connectivity matrices between motor cortex and prefrontal cortex were significantly increased after BCI training (p's < 0.05). In iTBS+BCI session, amplitudes of motor-evoked potentials were significantly increased after iTBS (p's < 0.05), but no change in HbO2 concentration or functional connectivity was observed throughout the whole session (p's > 0.05). Conclusions: To our knowledge, this is the first study that investigated how iTBS targeted on M1 influences BCI accuracy and the acute neuroplastic changes after BCI training. Our results revealed that iTBS targeted on M1 did not influence BCI accuracy or facilitate the neuroplastic changes after BCI training. Therefore, M1 might not be an effective stimulation target of iTBS for the purpose of improving BCI accuracy or facilitate its effectiveness; other brain regions (i.e., prefrontal cortex) are needed to be further investigated as potentially effective stimulation targets.

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.

[Research progress on multiscale entropy algorithm and its application in neural signal analysis]

Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.

Reduced Complexity in Stroke with Motor Deficits: A Resting-State fMRI Study

Recently, alterations of complexity due to brain disorders have been demonstrated using brain entropy (BEN), while the changes of brain complexity in stroke, a common cerebrovascular disease, remain unclear. In this research, resting-state functional magnetic resonance imaging (fMRI) was performed to explore the alterations of brain complexity using BEN in twenty stroke patients with motor deficits and nineteen matched healthy controls. The sample entropy (SampEn) was applied to build the BEN mapping for each participant. Compared with healthy controls, stroke patients exhibited lower BEN values in the contralesional precentral gyrus (preCG), bilateral dorsolateral frontal gyrus (SFGdor) and bilateral supplementary motor area (SMA). Moreover, significantly positive correlations between BEN values and Fugl-Meyer Assessment scores were detected in the ipsilesional SFGdor and ipsilesional SMA. Mutual information independence was observed between BEN and regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), respectively, in the stroke patients. Our findings implied that brain complexity had been impacted after stroke, and also suggested that BEN could be a complementary tool for evaluating the motor impairment after stroke.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker

OBJECTIVE

This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated.

METHODS

EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity.

RESULTS

ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values.

CONCLUSIONS

The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients.

SIGNIFICANCE

The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.

Assessing the Relationship Between Motor Anticipation and Cortical Excitability in Subacute Stroke Patients With Movement-Related Potentials

Background: Stroke survivors may lack the cognitive ability to anticipate the required control for palmar grasp execution. The cortical mechanisms involved in motor anticipation of palmar grasp movement and its association with post-stroke hand function remains unknown. Aims: To investigate the cognitive anticipation process during a palmar grasp task in subacute stroke survivors and to compare with healthy individuals. The association between cortical excitability and hand function was also explored. Methods: Twenty-five participants with hemiparesis within 1-6 months after first unilateral stroke were recruited. Twenty-five matched healthy individuals were recruited as control. Contingent negative variation (CNV) was measured using electroencephalography recordings (EEG). Event related potentials were elicited by cue triggered hand movement paradigm. CNV onset time and amplitude between pre-cue and before movement execution were recorded. Results: The differences in CNV onset time and peak amplitude were statistically significant between the subacute stroke and control groups, with patients showing earlier onset time with increased amplitudes. However, there was no statistically significant difference in CNV onset time and peak amplitude between lesioned and non-lesioned hemisphere in the subacute stroke group. Low to moderate linear associations were observed between cortical excitability and hand function. Conclusions: The earlier CNV onset time and higher peak amplitude observed in the subacute stroke group suggest increased brain computational demand during palmar grasp task. The lack of difference in CNV amplitude between the lesioned and non-lesioned hemisphere within the subacute stroke group may suggest that the non-lesioned hemisphere plays a role in the motor anticipatory process. The moderate correlations suggested that hand function may be associated with cortical processing of motor anticipation.

Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands

Many of the surgeries performed under general anesthesia are aided by electroencephalogram (EEG) monitoring. With increased focus on detecting the anesthesia states of patients in the course of surgery, more attention has been paid to analyzing the power spectra and entropy measures of EEG signal during anesthesia. In this paper, by using the relative power of EEG frequency bands and the EEG entropy measures, a new method for detecting the depth of anesthesia states has been presented based on the least squares support vector machine (LS-SVM) classifiers. EEG signals were recorded from 20 patients before, during and after general anesthesia in the operating room at a sampling rate of 200 Hz. Then, 12 features were extracted from each EEG segment, 10 s in length, which are used for anesthesia state monitoring. No significant difference was observed (p > 0.05) between these features and the bispectral index (BIS), which is the commonly used measure of anesthetic effect. The used LS-SVM classifier based method is able to identify the anesthesia states with an accuracy of 80% with reference to the BIS index. Since the underlying equation of the utilized LS-SVM is linear, the computational time of the algorithm is not significant and therefore it can be used for online application in operation rooms.

Four Channel Multivariate Coherence Training: Development and Evidence in Support of a New Form of Neurofeedback

As the field of neurofeedback and neuromodulation grows, trends toward using neurofeedback to treat problems of brain dysfunction have emerged. While the use of connectivity based fMRI guided neurofeedback has shown itself to be efficacious, the expense related to the treatment calls for a more practical solution. The use of QEEG guided neurofeedback in the treatment has shown promise as an emerging treatment. To date, EEG based neurofeedback approaches have used technology with limited sophistication. We designed a new form of neurofeedback that uses four channels of EEG with a multivariate calculation of coherence metrics. Following a mathematical presentation of this model, we present findings of a multi-site study with clinical subjects with various diagnoses. We compared this form of multivariate coherence neurofeedback to the more standard two channel coherence training. Findings showed that there was a significant difference between the groups with four channel multivariate coherence neurofeedback leading to greater changes in EEG metrics. Compared to two channel coherence training, four channel multivariate coherence neurofeedback led to a greater than 50% change in power and 400% in coherence values per session. The significance of these findings is discussed in relation to complex calculations of effective connectivity and how this might lead to even greater enhancements in neurofeedback efficacy.