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Mastropietro, A.; Pirovano, I.; Scano, A.; Guanziroli, E.; Molteni, F.; Re, R.; Rizzo, G. Electroencephalography for Neurological Disorders Rehabilitation. Encyclopedia. Available online: (accessed on 22 June 2024).
Mastropietro A, Pirovano I, Scano A, Guanziroli E, Molteni F, Re R, et al. Electroencephalography for Neurological Disorders Rehabilitation. Encyclopedia. Available at: Accessed June 22, 2024.
Mastropietro, Alfonso, Ileana Pirovano, Alessandro Scano, Eleonora Guanziroli, Franco Molteni, Rebecca Re, Giovanna Rizzo. "Electroencephalography for Neurological Disorders Rehabilitation" Encyclopedia, (accessed June 22, 2024).
Mastropietro, A., Pirovano, I., Scano, A., Guanziroli, E., Molteni, F., Re, R., & Rizzo, G. (2023, August 18). Electroencephalography for Neurological Disorders Rehabilitation. In Encyclopedia.
Mastropietro, Alfonso, et al. "Electroencephalography for Neurological Disorders Rehabilitation." Encyclopedia. Web. 18 August, 2023.
Electroencephalography for Neurological Disorders Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients’ state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. Electroencephalography (EEG) measures the electrical activity of the brain and can monitor the complex neuronal activity and its changes.

EEG rehabilitation stroke connectivity

1. Electroencephalography

Electroencephalography (EEG) is a technique that measures the electrical activity of the brain from electrodes attached to the scalp. EEG has a high temporal resolution, meaning that it can capture fast changes in brain activity with millisecond accuracy [1]. EEG signals reflect the synchronous activity of large populations of neurons, and they are characterized by different frequency bands that correspond to different physiological and behavioral conditions. For example, delta waves (0.5–4 Hz) are dominant during deep sleep, theta waves (4–8 Hz) are related to memory and emotion, alpha waves (8–13 Hz) indicate relaxed wakefulness, beta waves (13–30 Hz) reflect alertness and concentration, and gamma waves (30–150 Hz) are associated with cognitive processing and sensory integration [2].
With the advent of high-density systems, it is possible to monitor whole-brain neuronal activity with a better spatial resolution, which is more suitable for exploring functional network organization [3]. Indeed, EEG can also show how different brain regions communicate with each other, by measuring the functional or effective connectivity between them. Functional connectivity (FC) reflects the statistical dependence or correlation between two signals, while effective connectivity (EC) reflects the causal influence or direction of information flow between them [4]. EEG connectivity can be estimated with different metrics, such as coherence, phase synchronization, Granger causality, or transfer entropy [5][6], both in the time and frequency domain.
Moreover, graph analysis has been successfully employed to concisely describe the brain network’s integration and segregation behavior in communication [7]. The brain is thus described as a complex network, where specific cortex areas represent nodes and the links between these nodes represent the functional interaction between these cerebral regions. It has been found that the human brain exhibits a small-world behavior—a balance between a local and global integration of networks.
EEG is thus a valuable tool for studying how the brain changes and recovers its functions after injury or disease, such as stroke, Parkinson’s disease (PD), cerebral palsy (CP), spinal cord injury (SCI), or traumatic brain injury (TBI). EEG has several advantages over other neuroimaging techniques, such as being affordable, portable, easy to use, and adaptable to different situations. EEG can work both in rest and movement conditions, and it can be combined with other modalities, such as EMG, kinematics, functional NIRS (fNIRS), or transcranial magnetic stimulation (TMS). EEG can reveal various aspects of brain function that are relevant for motor rehabilitation, such as event-related potentials (ERPs), power spectra, and connectivity measures. ERPs are time-locked changes in EEG signals that reflect the brain’s response to specific stimuli or events. Power spectra show the distribution of EEG signal energy across different frequency bands. Connectivity measures show how different brain regions interact with each other. Figure 1 shows how EEG signals are processed and what kind of information can be obtained from them.
Figure 1. A schematic representation of EEG signal processing workflow and biomarkers. The figure shows the steps involved in acquiring, preprocessing, analyzing, and interpreting EEG signals for different applications in motor rehabilitation. The biomarkers include event-related potentials (ERP), power spectrum, functional connectivity, and graph analysis.

2. Applications of EEG in Rehabilitation

EEG is a widely used technique to study the brain changes that occur during and after post-stroke rehabilitation, across different stages of recovery [8]. EEG biomarkers, such as the ratio of slow (delta/theta) to fast (alpha/beta) waves, can predict motor outcomes in stroke patients, as they indicate the level of arousal and alertness of the brain [9][10]. EEG can also investigate other neurological disorders that affect motor function, such as PD, which is characterized by reduced motor-evoked potentials and altered EEG microstates [11]; CP, which shows abnormal EEG patterns and connectivity [12][13]; SCI, which affects the cortico-spinal communication and motor control [14]; and TBI, which disrupts the functional network organization and integration [15][16].
In addition to traditional biomarkers, more recently, FC investigation proved to be particularly interesting in the study of rehabilitation effects in those pathologies derived from the disruption of information transfer between brain regions and helped to explore how induced brain plasticity may play an important role in functionality recovery [17].
Brain connectivity in the resting state (RS) is one of the most investigated conditions since it is the easiest experimental protocol that can be performed with patients of all grades of impairments, and it has been demonstrated that RS connectivity is predictive of motor-function recovery in stroke patients [18]. Nevertheless, studies have also been conducted during the proper execution of tasks [19][20] or motor imagery [21] protocols to investigate movement-related network configuration.
Most of the studies evaluating the effect of rehabilitation in motor recovery focused their analysis on motor-network characterization. However, altered motor-network FC has also been found with higher-order cognitive control networks such as default mode networks, executive control networks, and dorsal attention networks. Therefore, connectivity patterns have been recently investigated both within and between RS large-scale networks [22][23][24].
In the literature, the major results of connectivity analysis in the rehabilitation field are focused on stroke recovery, comparing stroke patients with control groups [18][25][26][27] and evaluating the effect of different rehabilitation treatments [28][29][30]. In most works, an altered inter-hemispheric connectivity pattern was found. Homologous regions of the two hemispheres show reduced connectivity in the acute stage, which gradually returns to a normal level in sub-acute and chronic stages both during rest and motor execution [19][31][32]. Indeed, brain network reorganization has been demonstrated to depend on time after stroke [33], and an increase in inter-hemispheric connectivity, particularly between the primary motor cortexes in alpha and beta frequencies, was found to positively correlate with motor outcome improvement [20][31][32]. Conversely, an increase in RS-directed connectivity measures, from pre-motor towards primary motor intra-hemispheric regions, was found in sub-acute patients assessed before and after rehabilitation treatment [34]. Hoshino et al. in 2021 found higher intra-hemispheric FC in both hemispheres in RS and during ankle movement [20]. Wu et al. in 2015 found a positive correlation between motor outcome and an increase in coherence between ipsilesional pre-motor and primary motor cortexes in chronic patients after one month of rehabilitation [35]. This alteration in the communication may be due to alterations between the segregation and integration of information between affected and non-affected hemispheres [17].
Philips et al. in 2017 proved that topographical measures of integration and segregation among functional networks may be useful biomarkers of post-stroke motor recovery, suggesting their employment for the prognosis and evaluation of therapeutic outcomes [36]. Many studies employed graph analysis [37], reporting that small-worldness reduces in stroke patients when compared to healthy subjects [25][26]. Small-worldness in RS brain networks was also suggested to represent a biomarker of functional recovery in stroke patients since a correlation with motor outcome was found [26]. Molteni et al. found an increase in the node strength of the contralesional primary motor cortex and ipsilesional pre-frontal cortex after exoskeleton training in subjects with lesions in the non-dominant hemisphere, as well as a restoration of the interactions between primary motor and premotor cortexes after rehabilitation [29].
As for large-scale intra-network connectivity, Romeo et al. investigated the interaction of 14 RS networks and their correlation to different impairment domains in a cohort of 30 sub-acute/chronic stroke patients. Interestingly they found a correlation between dorsal attention networks and language network FC with motor indexes [24]. Wang et al. in 2018 explored neurological changes after guided or non-guided robot hand training in 24 chronic stroke patients. They found that only the robot-assisted group showed motor improvement and found an increase in the temporal variability of six large-scale networks, including somatomotor, attention, auditory, and default mode networks [22].


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