Small World derived index to distinguish Alzheimer's type dementia and healthy subjects.

BACKGROUND: This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. METHODS: Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. RESULTS: Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. CONCLUSIONS: These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.

Integration of cognitive-motor dual-task training in physical sessions of highly-skilled basketball players.

We investigated the effects of a cognitive-motor dual-task training (CMDT) integrated into a physical training circuit. Specific tests on sprint, agility, and cognitive processes associated with anticipatory event-related potential (ERP) components and behavioural performance during a cognitive discrimination response task (DRT) were evaluated before and after the intervention. Thirty skilled basketball players were recruited and divided into an experimental group executing the "physical CMDT" and a control group performing standard physical training. The CMDT session was performed by four athletes simultaneously who executed different circuits. One circuit was the CMDT, implemented with interactive devices thus engaging strong motor control, preparedness, and quick decision-making during task performance. Results on physical performance showed that only the experimental group improved in completion time on sprint (5.83%) and agility (3.55%) tests. At the brain level, we found that in the DRT the motor anticipation increased by over 50%, and the response time became 10% faster. Instead, regarding cognitive preparation, both protocols were equally effective and response accuracy parallelly increased in the post-test. In conclusion, the proposed "physical CMDT" integrated into a group session, can improve sprint and agility and the neural correlate of this effect is the increase of motor preparation in the premotor cortex in only five weeks.

Assistance Robotics and Biosensors 2019.

This Special Issue is focused on breakthrough developments in the field of assistive and rehabilitation robotics. The selected contributions include current scientific progress from biomedical signal processing and cover applications to myoelectric prostheses, lower-limb and upper-limb exoskeletons and assistive robotics.

Assistance Robotics and Biosensors.

This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain⁻machine interfacing.

Retrospective Multicenter Cohort Study on Safety and Electroencephalographic Response to Lacosamide for Neonatal Seizures.

BACKGROUND: There is growing evidence supporting the safety and effectiveness of lacosamide in older children. However, minimal data are available for neonates. We aimed to determine the incidence of adverse events associated with lacosamide use and explore the electroencephalographic seizure response to lacosamide in neonates. METHODS: A retrospective cohort study was conducted using data from seven pediatric hospitals from January 2009 to February 2020. For safety outcomes, neonates were followed for ≤30 days from index date. Electroencephalographic response of lacosamide was evaluated based on electroencephalographic reports for ≤3 days. RESULTS: Among 47 neonates, 98% received the first lacosamide dose in the intensive care units. During the median follow-up of 12 days, 19% of neonates died, and the crude incidence rate per 1000 patient-days (95% confidence interval) of the adverse events by diagnostic categories ranged from 2.8 (0.3, 10.2) for blood or lymphatic system disorders and nervous system disorders to 10.5 (4.2, 21.6) for cardiac disorders. Electroencephalographic seizures were observed in 31 of 34 patients with available electroencephalographic data on the index date. There was seizure improvement in 29% of neonates on day 1 and also in 29% of neonates on day 2. On day 3, there was no change in 50% of neonates and unknown change in 50% of neonates. CONCLUSIONS: The results are reassuring regarding the safety of lacosamide in neonates. Although some neonates had fewer seizures after lacosamide administration, the lack of a comparator arm and reliance on qualitative statements in electroencephalographic reports limit the preliminary efficacy results.

Frontal Activity of Recent Suicide Attempters: EEG spectrum Power Performing Raven Task.

Background: Deficits in problem-solving may be related to vulnerability to suicidal behavior. We aimed to identify the electroencephalographic (EEG) power spectrum associated with the performance of the Raven as a reasoning/problem-solving task among individuals with recent suicide attempts. Methods: This study with the case-control method, consisted of 61 participants who were assigned to three groups: Suicide attempt + Major Depressive Disorder (SA + MDD), Major Depressive Disorder (MDD), and Healthy Control (HC). All participants underwent clinical evaluations and problem-solving abilities. Subsequently, EEG signals were recorded while performing the Raven task. Results: The SA + MDD and MDD groups were significantly different from the HC group in terms of anxiety, reasons for life, and hopelessness. Regarding brain oscillations in performing the raven task, increased theta, gamma, and betha power extending over the frontal areas, including anterior prefrontal cortex, dlPFC, pre-SMA, inferior frontal cortex, and medial prefrontal cortex, was significant in SA + MDD compared with other groups. The alpha wave was more prominent in the left frontal, particularly in dlPFC in SA + MDD. Compared to the MDD group, the SA + MDD group had a shorter reaction time, while their response accuracy did not differ significantly. Conclusions: Suicidal patients have more frontal activity in planning and executive function than the two other groups. Nevertheless, it seems that reduced activity in the left frontal region, which plays a crucial role in managing emotional distress, can contribute to suicidal tendencies among vulnerable individuals. Limitation The small sample size and chosen difficult trials for the Raven task were the most limitations of the study.

Transcranial Alternating Current Stimulation Alters Auditory Steady-State Oscillatory Rhythms and Their Cross-Frequency Couplings.

Auditory cortical plasticity deficits in schizophrenia are evidenced with electroencephalographic (EEG)-derived biomarkers, including the 40-Hz auditory steady-state response (ASSR). Aiming to understand the underlying oscillatory mechanisms contributing to the 40-Hz ASSR, we examined its response to transcranial alternating current stimulation (tACS) applied bilaterally to the temporal lobe of 23 healthy participants. Although not responding to gamma tACS, the 40-Hz ASSR was modulated by theta tACS (vs sham tACS), with reductions in gamma power and phase locking being accompanied by increases in theta-gamma phase-amplitude cross-frequency coupling. Results reveal that oscillatory changes induced by frequency-tuned tACS may be one approach for targeting and modulating auditory plasticity in normal and diseased brains.

Poor reactivity of posterior electroencephalographic alpha rhythms during the eyes open condition in patients with dementia due to Parkinson's disease.

Here, we hypothesized that the reactivity of posterior resting-state electroencephalographic (rsEEG) alpha rhythms during the transition from eyes-closed to -open condition might be lower in patients with Parkinson's disease dementia (PDD) than in patients with Alzheimer's disease dementia (ADD). A Eurasian database provided clinical-demographic-rsEEG datasets in 73 PDD patients, 35 ADD patients, and 25 matched cognitively unimpaired (Healthy) persons. The eLORETA freeware was used to estimate cortical rsEEG sources. Results showed substantial (greater than -10%) reduction (reactivity) in the posterior alpha source activities from the eyes-closed to the eyes-open condition in 88% of the Healthy seniors, 57% of the ADD patients, and only 35% of the PDD patients. In these alpha-reactive participants, there was lower reactivity in the parietal alpha source activities in the PDD group than in the healthy control seniors and the ADD patients. These results suggest that PDD patients show poor reactivity of mechanisms desynchronizing posterior rsEEG alpha rhythms in response to visual inputs. That neurophysiological biomarker may provide an endpoint for (non) pharmacological interventions for improving vigilance regulation in those patients.

Brain structure and function in the fourth decade of life after extremely low birth weight: An MRI and EEG study.

OBJECTIVE: To examine potential long-term effects of extremely low birth weight (ELBW; ≤ 1000 g) on adult brain structure, brain function, and cognitive-behavioral performance. METHODS: A subset of survivors from the prospectively-followed McMaster ELBW Cohort (n = 23, MBW = 816 g) and their peers born at normal birth weight (NBW; ≥ 2500 g; n = 14, MBW = 3361 g) provided T1-weighted magnetic resonance imaging (MRI) brain scans, resting electroencephalographic (EEG) recordings, and behavioral responses to a face-processing task in their early thirties. RESULTS: Visual discrimination accuracy for human faces, resting EEG alpha power, and long-distance alpha coherence were lower in ELBW survivors than NBW adults, and volumes of white matter hypointensities (WMH) were higher. Across groups, face-processing performance was correlated positively with posterior EEG spectral power and long-distance alpha and theta coherence, and negatively with WMH. The associations between face-processing scores and parietal alpha power and theta coherence were reduced after adjustment for WMH. CONCLUSIONS: Electrocortical activity, brain functional connectivity, and higher-order processing ability may be negatively affected by WMH burden, which is greater in adults born extremely preterm. SIGNIFICANCE: Decrements in electrocortical activity and behavioral performance in adult ELBW survivors may be partly explained by increased WMH volumes in this vulnerable population.

Testing a Multicomponent Training Designed to Improve Sprint, Agility and Decision-Making in Elite Basketball Players.

This study tested if, in elite basketball players' training, the integration of a cognitive component within a multi-component training (MCT) could be more effective than an MCT with motor components only to improve both physical and cognitive skills. To this purpose, we designed an MCT focussed on sprint and agility incorporating a cognitive-motor dual-task training (CMDT) focussed on decision-making speed. Specific tests on sprint, agility and decision-making, and event-related potential (ERP) during the latter test were evaluated before and after the intervention. Thirty elite basketball players were recruited and divided into an experimental group executing CMDT integrated into the MCT and a control group performing the motor MCT (without cognitive components). The MCT with CMDT session was performed by four athletes simultaneously that executed different circuits. One circuit was the CMDT which was realized using interactive devices. Results on physical performance showed that only the experimental group improved in sprint and agility and also shortened response time in the decision-making test. At the neural level, the experimental group only shows an increase in the P3 ERP component, which has been associated with a series of post-perceptual cognitive functions, including decision-making. In conclusion, CMDT implemented within an MCT, likely stimulating more than physical training cortical plasticity, could be more effective than a motor MCT alone in improving the physical and cognitive skills of elite basketball players in five weeks only.

Reactivity of posterior cortical electroencephalographic alpha rhythms during eyes opening in cognitively intact older adults and patients with dementia due to Alzheimer's and Lewy body diseases.

Please modify the Abstract as follows:Here we tested if the reactivity of posterior resting-state electroencephalographic (rsEEG) alpha rhythms from the eye-closed to the eyes-open condition may differ in patients with dementia due to Lewy Bodies (DLB) and Alzheimer's disease (ADD) as a functional probe of the dominant neural synchronization mechanisms regulating the vigilance in posterior visual systems.We used clinical, demographical, and rsEEG datasets in 28 older adults (Healthy), 42 DLB, and 48 ADD participants. The eLORETA freeware was used to estimate cortical rsEEG sources.Results showed a substantial (> -10%) reduction in the posterior alpha activities during the eyes-open condition in 24 Healthy, 26 ADD, and 22 DLB subjects. There were lower reductions in the posterior alpha activities in the ADD and DLB groups than in the Healthy group. That reduction in the occipital region was lower in the DLB than in the ADD group.These results suggest that DLB patients may suffer from a greater alteration in the neural synchronization mechanisms regulating vigilance in occipital cortical systems compared to ADD patients.

Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.

Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

A novel approach to validate the efficacy of single task ERP paradigms to measure cognitive workload.

The present study examined the utility of a single-task paradigm to evaluate cognitive workload. The cognitive workload from twenty-five healthy participants was measured during a tilt-ball game while tones were presented in the background to generate event-related potentials (ERPs) in electroencephalographic (EEG) data. In the game, participants were instructed to move the ball to highlighted targets and avoid moving obstacles. The game's difficulty level was manipulated (easy, medium, hard) by adjusting the number and speed of the moving obstacles. The difficulty levels were presented in a random order during multiple short runs to minimize the effects of habituation, fatigue, and boredom. The behavioral results showed that greater task difficulty resulted in a significant decrease (p < 0.001) in game performance, i.e., participants achieved few targets with a high collision rate. To evaluate cognitive workload, we measured the amplitude of early ERP components (N1, P1, and P2) corresponding to the involuntary attention orienting response. The amplitude of the N1 component decreased significantly (p = 0.029) with an increase in cognitive workload. These findings suggest that the early ERP component, specifically the N1, corresponds to attention orienting response, and that the task difficulty modulates it. This study provided evidence that the inverse relationship between ERP components and cognitive workload can be reliably assessed by controlling for other factors such as habituation or boredom during a single task paradigm.

Abnormalities of resting-state EEG in patients with prodromal and overt dementia with Lewy bodies: Relation to clinical symptoms.

OBJECTIVE: Here we tested if cortical sources of resting state electroencephalographic (rsEEG) rhythms may differ in sub-groups of patients with prodromal and overt dementia with Lewy bodies (DLB) as a function of relevant clinical symptoms. METHODS: We extracted clinical, demographic and rsEEG datasets in matched DLB patients (N = 60) and control Alzheimer's disease (AD, N = 60) and healthy elderly (Nold, N = 60) seniors from our international database. The eLORETA freeware was used to estimate cortical rsEEG sources. RESULTS: As compared to the Nold group, the DLB and AD groups generally exhibited greater spatially distributed delta source activities (DLB > AD) and lower alpha source activities posteriorly (AD > DLB). As compared to the DLB "controls", the DLB patients with (1) rapid eye movement (REM) sleep behavior disorders showed lower central alpha source activities (p < 0.005); (2) greater cognitive deficits exhibited higher parietal and central theta source activities as well as higher central, parietal, and occipital alpha source activities (p < 0.01); (3) visual hallucinations pointed to greater parietal delta source activities (p < 0.005). CONCLUSIONS: Relevant clinical features were associated with abnormalities in spatial and frequency features of rsEEG source activities in DLB patients. SIGNIFICANCE: Those features may be used as neurophysiological surrogate endpoints of clinical symptoms in DLB patients in future cross-validation prospective studies.

Specific EEG markers in POLG1 Alpers' syndrome.

OBJECTIVE: To examine whether rhythmic high-amplitude delta with superimposed (poly)spikes (RHADS) in EEG allow a reliable early diagnosis of Alpers-Huttenlocher syndrome (AHS) and contribute to recognition of this disease. METHODS: EEGs of nine patients with DNA-proven AHS and fifty age-matched patients with status epilepticus were retrospectively examined by experts for the presence of RHADS and for accompanying clinical signs and high-frequency ripples. Reproducibility of RHADS identification was tested in a blinded panel. RESULTS: Expert defined RHADS were found in at least one EEG of all AHS patients and none of the control group. RHADS were present at first status epilepticus in six AHS patients (67%). Sometimes they appeared 5-10 weeks later and disappeared over time. RHADS were symptomatic in three AHS patients and five AHS patients showed distinct ripples on the (poly)spikes of RHADS. Independent RHADS identification by the blinded panel resulted in a sensitivity of 87.5% (95% CI 47-100) and a specificity of 87.5% (95% CI 77-94) as compared to the experts' reporting. CONCLUSION: RHADS are a highly specific EEG phenomenon for diagnosis of AHS and can be reliably recognized. Clinical expression and EEG ripples suggest that they signify an epileptic phenomenon. SIGNIFICANCE: RHADS provide a specific tool for AHS diagnosis.

An effective approach to classify epileptic EEG signal using local neighbor gradient pattern transformation methods.

Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed. Extracted features are fed into machine learning algorithms like k-nearest neighbor (k-NN), quadratic linear discriminant analysis, support vector machine, ensemble classifier and artificial neural network (ANN) to classify the EEG signals. In this study, the classification performance for 17 different cases using 10-fold cross validation with the following classification problems are executed (i) healthy-ictal, (ii) interictal-ictal, (iii) healthy-interictal, (iv) seizure free-ictal and (v) healthy-interictal-ictal. The experimental result shows that in all the cases LNGP and SWLNGP attained higher classification accuracy using ANN. Further, the computational performance and the classification accuracy of the proposed methods are compared with the recently proposed techniques for epileptic detection. It shows that the performance of LNGP and SWLNGP method with ANN classifier are superior over other recently proposed techniques for the aforesaid problems. Hence, the proposed methods are simple, fast, reliable and easily implementable for real-time epileptic detection.

Is motor imagery different in catatonic schizophrenia?

Catatonic schizophrenia, a rare subtype in this disease group, is characterized by motor disturbances. The current study investigated the reactivity of electroencephalographic mu rhythm in a motion imagery task in two single cases of first-episode catatonic schizophrenia, assuming they would show less mu rhythm reduction compared to paranoid schizophrenic patients and healthy controls.

On-going electroencephalographic rhythms related to cortical arousal in wild-type mice: the effect of aging.

Resting state electroencephalographic (EEG) rhythms reflect the fluctuation of cortical arousal and vigilance in a typical clinical setting, namely the EEG recording for few minutes with eyes closed (i.e., passive condition) and eyes open (i.e., active condition). Can this procedure be back-translated to C57 (wild type) mice for aging studies? On-going EEG rhythms were recorded from a frontoparietal bipolar channel in 85 (19 females) C57 mice. Male mice were subdivided into 3 groups: 25 young (4.5-6 months), 18 middle-aged (12-15 months), and 23 old (20-24 months) mice to test the effect of aging. EEG power density was compared between short periods (about 5 minutes) of awake quiet behavior (passive) and dynamic exploration of the cage (active). Compared with the passive condition, the active condition induced decreased EEG power at 1-2 Hz and increased EEG power at 6-10 Hz in the group of 85 mice. Concerning the aging effects, the passive condition showed higher EEG power at 1-2 Hz in the old group than that in the others. Furthermore, the active condition exhibited a maximum EEG power at 6-8 Hz in the former group and 8-10 Hz in the latter. In the present conditions, delta and theta EEG rhythms reflected changes in cortical arousal and vigilance in freely behaving C57 mice across aging. These changes resemble the so-called slowing of resting state EEG rhythms observed in humans across physiological and pathological aging. The present EEG procedures may be used to enhance preclinical phases of drug discovery in mice for understanding the neurophysiological effects of new compounds against brain aging.

Age-Related Inter-Region EEG Coupling Changes During the Control of Bottom-Up and Top-Down Attention.

We investigated age-related changes in electroencephalographic (EEG) coupling of theta-, alpha-, and beta-frequency bands during bottom-up and top-down attention. Arrays were presented with either automatic "pop-out" (bottom-up) or effortful "search" (top-down) behavior to younger and older participants. The phase-locking value was used to estimate coupling strength between scalp recordings. Behavioral performance decreased with age, with a greater age-related decline in accuracy for the search than for the pop-out condition. Aging was associated with a declined coupling strength of theta and alpha frequency bands, with a greater age-related decline in whole-brain coupling values for the search than for the pop-out condition. Specifically, prefronto-frontal coupling in theta- and alpha-bands, fronto-parietal and parieto-occipital couplings in beta-band for younger group showed a right hemispheric dominance, which was reduced with aging to compensate for the inhibitory dysfunction. While pop-out target detection was mainly associated with greater parieto-occipital beta-coupling strength compared to search condition regardless of aging. Furthermore, prefronto-frontal coupling in theta-, alpha-, and beta-bands, and parieto-occipital coupling in beta-band functioned as predictors of behavior for both groups. Taken together these findings provide evidence that prefronto-frontal coupling of theta-, alpha-, and beta-bands may serve as a possible basis of aging during visual attention, while parieto-occipital coupling in beta-band could serve for a bottom-up function and be vulnerable to top-down attention control for younger and older groups.