Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease.

BACKGROUND: Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES: The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS: Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS: FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS: Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Bimodal sensory integration in migraine: A study of the effect of visual stimulation on somatosensory evoked cortical responses.

BACKGROUND: Merging of sensory information is a crucial process for adapting the behaviour to the environment in all species. It is not known if this multisensory integration might be dysfunctioning interictally in migraine without aura, where sensory stimuli of various modalities are processed abnormally when delivered separately. To investigate this question, we compared the effects of a concomitant visual stimulation on conventional low-frequency somatosensory evoked potentials and embedded high-frequency oscillations between migraine patients and healthy volunteers. METHODS: We recorded somatosensory evoked potentials in 19 healthy volunteers and in 19 interictal migraine without aura patients before, during, and 5 min after (T2) simultaneous synchronous pattern-reversal visual stimulation. At each time point, we measured amplitude and habituation of the N20-P25 low-frequency-somatosensory evoked potentials component and maximal peak-to-peak amplitude of early and late bursts of high-frequency oscillations. RESULTS: In healthy volunteers, the bimodal stimulation significantly reduced low-frequency-somatosensory evoked potentials habituation and tended to reduce early high-frequency oscillations that reflect thalamocortical activity. By contrast, in migraine without aura patients, bimodal stimulation significantly increased low-frequency-somatosensory evoked potentials habituation and early high-frequency oscillations. At T2, all visual stimulation-induced changes of somatosensory processing had vanished. CONCLUSION: These results suggest a malfunctioning multisensory integration process, which could be favoured by an abnormal excitability level of thalamo-cortical loops.

A Hypothalamic Mechanism Regulates the Duration of a Migraine Attack: Insights from Microstructural and Temporal Complexity of Cortical Functional Networks Analysis.

The role of the hypothalamus and the limbic system at the onset of a migraine attack has recently received significant interest. We analyzed diffusion tensor imaging (DTI) parameters of the entire hypothalamus and its subregions in 15 patients during a spontaneous migraine attack and in 20 control subjects. We also estimated the non-linear measure resting-state functional MRI BOLD signal's complexity using Higuchi fractal dimension (FD) and correlated DTI/fMRI findings with patients' clinical characteristics. In comparison with healthy controls, patients had significantly altered diffusivity metrics within the hypothalamus, mainly in posterior ROIs, and higher FD values in the salience network (SN). We observed a positive correlation of the hypothalamic axial diffusivity with migraine severity and FD of SN. DTI metrics of bilateral anterior hypothalamus positively correlated with the mean attack duration. Our results show plastic structural changes in the hypothalamus related to the attacks severity and the functional connectivity of the SN involved in the multidimensional neurocognitive processing of pain. Plastic changes to the hypothalamus may play a role in modulating the duration of the attack.

Effects on Motor Control of Personalized Neuromodulation Against Multiple Sclerosis Fatigue.

Fatigue is a hidden symptom of Multiple Sclerosis (MS) disease that nevertheless impacts severely on patients' everyday life. Evidence indicates the involvement of the sensorimotor network and its inter-nodes communication at the basis of this symptom. Two randomized controlled trials (RCTs) showed that the personalized neuromodulation called Fatigue Relief in Multiple Sclerosis (FaReMuS) efficaciously fights multiple sclerosis (MS) fatigue. By this Proof of Concept study, we tested whether FaReMuS reverts the alteration of the brain-muscular synchronization previously observed occurring with fatigue. The cortico muscular coherence (CMC) was studied in 11 patients before and after FaReMuS, a 5-day tDCS (1.5 mA, 15 min per day) anodal over the whole body's somatosensory representation (S1) via a personalized MRI-based electrode (35 cm2) against the occipital cathode (70 cm2). Before FaReMuS, the CMC was observed at a mean frequency of 31.5 ± 1.6 Hz (gamma-band) and positively correlated with the level of fatigue (p = .027). After FaReMuS, fatigue reduced in average of 28% ± 33% the baseline level, and the CMC frequency reduced to 26.6 ± 1.5 Hz (p = .022), thus forthcoming the physiological beta-band as observed in healthy people. The personalized S1 neuromodulation treatment, ameliorating the central-peripheral communication that subtends simple everyday movements, supports the appropriateness of neuromodulations aiming at increasing the parietal excitability in fighting MS fatigue. The relationship between central-peripheral features and fatigue profile strengthens a central more than peripheral origin of the symptom.

Thalamo-cortical networks in subtypes of migraine with aura patients.

BACKGROUND: We searched for differences in resting-state functional connectivity (FC) between brain networks and its relationship with the microstructure of the thalamus between migraine with pure visual auras (MA), and migraine with complex neurological auras (MA+), i.e. with the addition of at least one of sensory or language symptom. METHODS: 3T MRI data were obtained from 20 patients with MA and 15 with MA + and compared with those from 19 healthy controls (HCs). We collected resting state data among independent component networks. Diffusivity metrics of bilateral thalami were calculated and correlated with resting state ICs-Z-scores. RESULTS: As compared to HCs, both patients with MA and MA + disclosed disrupted FC between the default mode network (DMN) and the right dorsal attention system (DAS). The MA + subgroup had lower microstructural metrics than both HCs and the MA subgroup, which correlated negatively with the strength of DMN connectivity. Although the microstructural metrics of MA patients did not differ from those of HCs, these patients lacked the correlation with the strength of DAS connectivity found in HCs. CONCLUSIONS: The present findings suggest that, as far as MRI profiles are concerned, the two clinical phenotypes of migraine with aura have both common and distinct morpho-functional features of nodes in the thalamo-cortical network.

Frequency-dependent functional connectivity in resting state networks.

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large-scale networks, called resting-state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency-dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band-limited power correlations from high-density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Haemodynamic activity characterization of resting state networks by fractal analysis and thalamocortical morphofunctional integrity in chronic migraine.

BACKGROUND: Chronic migraine (CM) can be associated with aberrant long-range connectivity of MRI-derived resting-state networks (RSNs). Here, we investigated how the fractal dimension (FD) of blood oxygenation level dependent (BOLD) activity may be used to estimate the complexity of RSNs, reflecting flexibility and/or efficiency in information processing in CM patients respect to healthy controls (HC). METHODS: Resting-state MRI data were collected from 20 untreated CM without history of medication overuse and 20 HC. On both groups, we estimated the Higuchi's FD. On the same subjects, fractional anisotropy (FA) and mean diffusivity (MD) values of bilateral thalami were retrieved from diffusion tensor imaging and correlated with the FD values. RESULTS: CM showed higher FD values within dorsal attention system (DAS) and the anterior part of default-mode network (DMN), and lower FD values within the posterior DMN compared to HC. Although FA and MD were within the range of normality, both correlated with the FD values of DAS. CONCLUSIONS: FD of DAS and DMN may reflect disruption of cognitive control of pain in CM. Since the normal microstructure of the thalamus and its positive connectivity with the cortical networking found in our CM patients reminds similar results obtained assessing the same structures but with the methods of neurophysiology, in episodic migraine during an attack, this may be yet another evidence in supporting CM as a never-ending migraine attack.

A New, High-Efficacy, Noninvasive Transcranial Electric Stimulation Tuned to Local Neurodynamics.

In this paper, we pose the following working hypothesis: in humans, transcranial electric stimulation (tES) with a time course that mimics the endogenous activity of its target is capable of altering the target's excitability. In our case, the target was the primary motor cortex (M1). We identified the endogenous neurodynamics of hand M1's subgroups of pyramidal neuronal pools in each of our subjects by applying Functional Source Separation (FSS) to their EEG recordings. We then tested whether the corticospinal excitability of the hand representation under the above described stimulation, which we named transcranial individual neurodynamics stimulation (tIDS), was higher than in the absence of stimulation (baseline). As a check, we compared tIDS with the most efficient noninvasive facilitatory corticospinal tES known so far, which is 20 Hz transcranial alternating current stimulation (tACS). The control conditions were as follows: (1) sham, (2) transcranial random noise stimulation (tRNS) in the same frequency range as tIDS (1-250 Hz), and (3) a low current tIDS (tIDSlow). Corticospinal excitability was measured with motor-evoked potentials under transcranial magnetic stimulation. The mean motor-evoked potential amplitude increase was 31% of the baseline during tIDS (p < 0.001), and it was 15% during tACS (p = 0.096). tRNS, tIDSlow, and sham induced no effects. Whereas tACS did not produce an enhancement in any subject at the individual level, tIDS was successful in producing an enhancement in 8 of the 16 subjects. The results of the present proof-of-principle study showed that proper exploitation of local neurodynamics can enhance the efficacy of personalized tES.SIGNIFICANCE STATEMENT This study demonstrated that, in humans, transcranial individual neurodynamics stimulation (tIDS), which mimics the endogenous dynamics of the target neuronal pools, effectively changes the excitability of these pools. tIDS holds promise for high-efficacy personalized neuromodulations based on individual local neurodynamics.

P3b amplitude as a signature of cognitive decline in the older population: An EEG study enhanced by Functional Source Separation.

With the greying population, it is increasingly necessary to establish robust and individualized markers of cognitive decline. This requires the combination of well-established neural mechanisms, and the development of increasingly sensitive methodologies. The P300 event-related potential (ERP) has been one of the most heavily investigated neural markers of attention and cognition, and studies have reliably shown that changes in the amplitude and latency of the P300 ERP index the process of aging. However, it is still not clear whether either the P3a or P3b sub-components additionally index levels of cognitive impairment. Here, we used a traditional visual three-stimulus oddball paradigm to investigate both the P3a and P3b ERP components in sixteen young and thirty-four healthy elderly individuals with varying degrees of cognitive ability. EEG data extraction was enhanced through the use of a novel signal processing method called Functional Source Separation (FSS) that increases signal-to-noise ratio by using a weighted sum of all electrodes rather than relying on a single, or a small sub-set, of EEG channels. Whilst clear differences in both the P3a and P3b ERPs were seen between young and elderly groups, only P3b amplitude differentiated older people with low memory performance relative to IQ from those with consistent memory and IQ. A machine learning analysis showed that P3b amplitude (derived from FSS analysis) could accurately categorise high and low performing elderly individuals (78% accuracy). A comparison of Bayes Factors found that differences in cognitive decline within the elderly group were 87 times more likely to be detected using FSS compared to the best performing single electrode (Cz). In conclusion, we propose that P3b amplitude could be a sensitive marker of early, age-independent, episodic memory dysfunction within a healthy older population. In addition, we advocate for the use of more advanced signal processing methods, such as FSS, for detecting subtle neural changes in clinical populations.

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain.

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.

Personalized, bilateral whole-body somatosensory cortex stimulation to relieve fatigue in multiple sclerosis.

BACKGROUND: The patients suffering from multiple sclerosis (MS) often consider fatigue the most debilitating symptom they experience, but conventional medicine currently offers poorly efficacious therapies. OBJECTIVE: We executed a replication study of an innovative approach for relieving MS fatigue. METHODS: According to the sample size estimate, we recruited 10 fatigued MS patients who received 5-day transcranial direct current stimulation (tDCS) in a randomized, double-blind, Sham-controlled, crossover study, with modified Fatigue Impact Scale (mFIS) score reduction at the end of the treatment as primary outcome. A personalized anodal electrode, shaped on the magnetic resonance imaging (MRI)-derived individual cortical folding, targeted the bilateral whole-body primary somatosensory cortex (S1) with an occipital cathode. RESULTS: The amelioration of fatigue symptoms after Real stimulation (40% of baseline) was significantly larger than after Sham stimulation (14%, p = 0.012). Anodal whole body S1 induced a significant fatigue reduction in mildly disabled MS patients when the fatigue-related symptoms severely hampered their quality of life. CONCLUSION: This second result in an independent group of patients supports the idea that neuromodulation interventions that properly select a personalized target might be a suitable non-pharmacological treatment for MS fatigue.

fMRI characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback.

A bilateral visuo-parietal-motor network is responsible for fine control of hand movements. However, the sub-regions which are devoted to maintenance of contraction stability and how these processes fluctuate with trial-quality of task execution and in the presence/absence of visual feedback remains unclear. We addressed this by integrating behavioural and fMRI measurements during right-hand isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback of the applied force. We quantified single-trial behavioural performance during 1) the whole task period and 2) stable contraction maintenance, and regressed these metrics against the fMRI data to identify the brain activity most relevant to trial-by-trial fluctuations in performance during specific task phases. fMRI-behaviour correlations in a bilateral network of visual, premotor, primary motor, parietal and inferior frontal cortical regions emerged during performance of the entire feedback task, but only in premotor, parietal cortex and thalamus during the stable contraction period. The trials with the best task performance showed increased bilaterality and amplitude of fMRI responses. With feedback, stronger BOLD-behaviour coupling was found during 10% compared to 30% contractions. Only a small subset of regions in this network were weakly correlated with behaviour without feedback, despite wider network activated during this task than in the presence of feedback. These findings reflect a more focused network strongly coupled to behavioural fluctuations when providing visual feedback, whereas without it the task recruited widespread brain activity almost uncoupled from behavioural performance.

Detecting large-scale networks in the human brain using high-density electroencephalography.

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

Impaired brainstem and thalamic high-frequency oscillatory EEG activity in migraine between attacks.

Introduction We investigated whether interictal thalamic dysfunction in migraine without aura (MO) patients is a primary determinant or the expression of its functional disconnection from proximal or distal areas along the somatosensory pathway. Methods Twenty MO patients and twenty healthy volunteers (HVs) underwent an electroencephalographic (EEG) recording during electrical stimulation of the median nerve at the wrist. We used the functional source separation algorithm to extract four functionally constrained nodes (brainstem, thalamus, primary sensory radial, and primary sensory motor tangential parietal sources) along the somatosensory pathway. Two digital filters (1-400 Hz and 450-750 Hz) were applied in order to extract low- (LFO) and high- frequency (HFO) oscillatory activity from the broadband signal. Results Compared to HVs, patients presented significantly lower brainstem (BS) and thalamic (Th) HFO activation bilaterally. No difference between the two cortical HFO as well as in LFO peak activations between the two groups was seen. The age of onset of the headache was positively correlated with HFO power in the right brainstem and thalamus. Conclusions This study provides evidence for complex dysfunction of brainstem and thalamocortical networks under the control of genetic factors that might act by modulating the severity of migraine phenotype.

Removing speech artifacts from electroencephalographic recordings during overt picture naming.

A number of electroencephalography (EEG) studies have investigated the time course of brain activation during overt word production. The interpretation of their results is complicated by the fact that articulatory movements may mask the cognitive components of interest. The first aim of the present study was to investigate when speech artifacts occur during word production planning and what effects they have on the spatio-temporal neural activation pattern. The second aim was to propose a new method that strongly attenuates speech artifacts during overt picture naming and to compare it with existing methods. EEG and surface electromyograms (EMGs) of the lips were recorded while participants overtly named pictures in a picture-word interference paradigm. The comparison of the raw data with lip EMG and the comparison of source localizations of raw and corrected EEG data showed that speech artifacts occurred mainly from ~400 ms post-stimulus onset, but some earlier artifacts mean that they occur much earlier than hitherto assumed. We compared previously used methods of speech artifacts removal (SAR) with a new method, which is based on Independent Component Analysis (SAR-ICA). Our new method clearly outperformed other methods. In contrast to other methods, there was only a weak correlation between the lip EMG and the corrected data by SAR-ICA. Also, only the data corrected with our method showed activation of cerebral sources consistent with meta-analyses of word production.

The spontaneous fluctuation of the excitability of a single node modulates the internodes connectivity: a TMS-EEG study.

Brain effective connectivity can be tracked by cerebral recruitments evoked by transcranial magnetic stimulation (TMS), as measured by simultaneous electroencephalography (TMS-EEG). When TMS is targeting the primary motor area, motor evoked potentials (MEPs) can be collected from the "target" muscles. The aim of this study was to measure whether or not effective brain connectivity changes with the excitability level of the corticospinal motor pathway (CSMP) as parameterized by MEP amplitude. After averaging two subgroups of EEG-evoked responses corresponding to high and low MEP amplitudes, we calculated the individual differences between them and submitted the grand average to sLORETA algorithm obtaining localized regions of interest (RoIs). Statistical differences of RoI recruitment strength between low and high CSMP excitation was assessed in single subjects. Preceding the feedback arrival, neural recruitment for stronger CSMP activation were weaker at 6-10 ms of homotopic sensorimotor areas BA3/4/5 of the right nonstimulated hemisphere (trend), weaker at 18-25 ms of left parietal BA2/3/40, and stronger at 26-32 ms of bilateral frontal motor areas BA6/8. The proposed method enables the tracking of brain network connectivity during stimulation of one node by measuring the strength of the connected recruited node activations. Spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. The effective connectivity within bilateral circuits orchestrating hand control appeared dynamically modulated in time even in resting state as probed by TMS.

Fractal Time Series: Background, Estimation Methods, and Performances.

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Clinical Sensitivity of Fractal Neurodynamics.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.

BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS: In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS: Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS: These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension.

Objectives: An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods: EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, ß, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.