Perturbation-evoked potentials: Significance and application in balance control research

Varghese, Jessy Parokaran; McIlroy, Robert E; Barnett‐Cowan, Michael

doi:10.1016/j.neubiorev.2017.10.022

Cited by 53 publications

(53 citation statements)

References 69 publications

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“…The detection of near falls is an emerging area of research that is contextually growing with the development of an increasing number of miniaturized and power-efficient wearable devices [7]. Supported by accumulated evidence on fall detection [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], the clinical utility of this investigation involves the unobtrusive and continuous monitoring of activities of daily life in populations at high risk of falling. This kind of strategy (i.e., near fall detection) can be useful to identify issues to be further addressed to prevent falls, associated injuries or simply improve the efficiency of already existing pre-impact fall-detection architectures.…”

Section: Discussionmentioning

confidence: 99%

“…The cortical unit analyzes these artifacts-free brain signals, quantifying the rate of variation in the EEGs power within the five bands of interest identified by authors in [18][19][20][21][22][23]: θ (4-7 Hz), α (8-12 Hz), β I, β II, β III (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). In more detail, the power spectrum density measurements are done by applying a sliding-window fast Fourier transform (FFT) on the considered EEG subset.…”

Section: Cortical Involvement Assessmentmentioning

confidence: 99%

“…The double-check implementation (i.e., generalization and lateralization) is justified by literature findings [19][20][21][22][23], which demonstrated that a reactive response leads to a widespread cortical involvement, while during unperturbed steps or non-challenging ADL, the cortical response remains more lateralized according to the limbs involved in the movement.…”

Section: Cortical Scoring Sectionmentioning

confidence: 99%

“…The architecture proposed here exploits medical evidence from recent studies [18][19][20][21][22][23], according to which the cerebral cortex can regulate the postural stability according to environmental demands [18]. Specifically, the authors in [19,20] proved that low-frequency cortical rhythms (f < 13 Hz) are related to perception and cognitive control. In the loss of balance context, the modulation of the bands θ (4-7 Hz) and α (8-12 Hz) seems to be related to the visual field stabilization and active decoding of data from the vestibular system.…”

mentioning

confidence: 99%

“…The unit dedicated to the muscular characterization has two main roles: realizing a binary ON/OFF pattern from muscular activity and triggering the cortical analysis unit. Once triggered, this latter unit quantifies the cortical involvements as the rate of variation in the EEG power spectrum density (PSD), considering the five bands of interest identified by authors in [18][19][20][21][22][23]. The parameters extracted from these units define some neuromuscular features of the subject under monitoring.…”

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confidence: 99%

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High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

Venuto

Mezzina

2020

Sensors

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Falls are a significant cause of loss of independence, disability and reduced quality of life in people with Parkinson’s disease (PD). Intervening quickly and accurately on the postural instability could strongly reduce the consequences of falls. In this context, the paper proposes and validates a novel architecture for the reliable recognition of losses of balance situations. The proposed system addresses some challenges related to the daily life applicability of near-fall recognition systems: the high specificity and system robustness against the Activities of Daily Life (ADL). In this respect, the proposed algorithm has been tested on five different tasks: walking steps, sudden curves, chair transfers via the timed up and go (TUG) test, balance-challenging obstacle avoidance and slip-induced loss of balance. The system analyzes data from wireless acquisition devices that capture electroencephalography (EEG) and electromyography (EMG) signals. The collected data are sent to two main units: the muscular unit and the cortical one. The first realizes a binary ON/OFF pattern from muscular activity (10 EMGs) and triggers the cortical unit. This latter unit evaluates the rate of variation in the EEG power spectrum density (PSD), considering five bands of interest. The neuromuscular features are then sent to a logical network for the final classification, which distinguishes among falls and ADL. In this preliminary study, we tested the proposed model on 9 healthy subjects (aged 26.3 ± 2.4 years), even if the study on PD patients is under investigation. Experimental validation on healthy subjects showed that the system reacts in 370.62 ± 60.85 ms with a sensitivity of 93.33 ± 5.16%. During the ADL tests the system showed a specificity of 98.91 ± 0.44% in steady walking steps recognition, 99.61 ± 0.66% in sudden curves detection, 98.95 ± 1.27% in contractions related to TUG tests and 98.42 ± 0.90% in the obstacle avoidance protocol.

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Section: Discussionmentioning

confidence: 99%

Section: Cortical Involvement Assessmentmentioning

confidence: 99%

Section: Cortical Scoring Sectionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

Venuto

Mezzina

2020

Sensors

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Acute fatigue alters brain activity and impairs reactive balance test performance

et al. 2021

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Conflicting results in Y‐Balance Test (YBT) performance are found when participants are exposed to acute physical fatigue (APF). Even though APF is known to impair isolated neurocognitive performance, the effects of APF on reactive balance test (RBT) performance have never been investigated. Furthermore, research on the underlying APF‐induced neurophysiological mechanisms during these functional tests is scarce to non‐existent. Therefore, the aim was to assess the influence of APF on two clinician‐friendly balance tests: the YBT and RBT, as well as its effects on brain activity. Twenty healthy adults participated in this randomized crossover study. APF was induced by a 30 seconds modified Wingate test. YBT performance was expressed in maximal reach distances, and RBT performance involved visuomotor reaction time and accuracy. Electroencephalography (EEG) was used to measure brain activity during these balance tests. Balance test performance and EEG recordings were analyzed pre‐post. APF was successfully induced given that secondary outcome measures (eg, heart rate, blood lactate concentration) significantly increased following the modified Wingate. Decreased RBT accuracy was observed after APF, yet YBT performance and RBT visuomotor reaction time were unaffected. APF induced spectral power increments in the brain during both YBT and RBT performance.

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Perturbation-Evoked Potentials: Future Usage in Human-Machine Interaction

Ditz

Müller-Putz

2019

Information Systems and Neuroscience

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Perturbation-evoked potentials: Significance and application in balance control research

Cited by 53 publications

References 69 publications

High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

High-Specificity Digital Architecture for Real-Time Recognition of Loss of Balance Inducing Fall

Acute fatigue alters brain activity and impairs reactive balance test performance

Perturbation-Evoked Potentials: Future Usage in Human-Machine Interaction

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