Induced gamma band activity from EEG as a possible index of training-related brain plasticity in motor tasks

Amo, Carlos; Santiago, Luis de; Luciáñez, Daniel Zarza; Alonso-Cortés, José Miguel León; Alonso‐Alonso, Miguel; Barea, Rafael; Boquete, Luciano

doi:10.1371/journal.pone.0186008

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…S7, we note that gamma band was the most commonly selected frequency band by the algorithm among all participants. Numerous studies have experimentally linked gamma band to a wide range of cognitive processes that include learning, attention, memory, and visual-auditory perception (24). However, this band seems to be particularly relevant for enhanced processing of attended attention (25,26).…”

Section: Discussionmentioning

confidence: 99%

BMI control of a third arm for multitasking

2018

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Brain-machine interface (BMI) systems have been widely studied to allow people with motor paralysis conditions to control assistive robotic devices that replace or recover lost function but not to extend the capabilities of healthy users. We report an experiment in which healthy participants were able to extend their capabilities by using a noninvasive BMI to control a human-like robotic arm and achieve multitasking. Experimental results demonstrate that participants were able to reliably control the robotic arm with the BMI to perform a goal-oriented task while simultaneously using their own arms to do a different task. This outcome opens possibilities to explore future human body augmentation applications for healthy people that not only enhance their capability to perform a particular task but also extend their physical capabilities to perform multiple tasks simultaneously.

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Section: Discussionmentioning

confidence: 99%

BMI control of a third arm for multitasking

2018

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“…Brain rhythms beyond 50 Hz are seldom studied with scalp EEG due to assumptions that neural activity in this range would have very low signal amplitudes and due to difficulties in removing muscular artifacts within this frequency range. However, recent studies have shown that EEG can be used to detect gamma band neural activity that is related to movements [47][48][49][50]. Gamma band activity recorded with EEG has been suggested to be potentially coupled with delta band phase [51,52].…”

Section: Introductionmentioning

confidence: 99%

Regression-based reconstruction of human grip force trajectories with noninvasive scalp electroencephalography

et al. 2019

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Objective. Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual's grip forces produced by the hand. Approach. Brain rhythms and grip forces were recorded from able-bodied human subjects while they performed an isometric force production task and a grasp-and-lift task. Grip force trajectories were reconstructed with a linear model that incorporated delta band (0.1-1 Hz) voltage potentials and spectral power in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma (30-50 Hz), mid gamma (70-110 Hz), and high gamma (130-200 Hz) bands. Trajectory reconstruction models were trained and tested through 10-fold cross validation. Main results. Modest accuracies were attained in reconstructing grip forces during isometric force production (median r = 0.42), and the grasp-and-lift task (median r = 0.51). Predicted trajectories were also analyzed further to assess the linear models' performance based on task requirements. For the isometric force production task, we found that predicted grip trajectories did not yield static grip forces that were distinguishable in magnitude across three task conditions. For the grasp-and-lift task, we estimate there would be an approximate 25% error in distinguishing when a user wants to hold or release an object. Significance. These findings indicate that EEG, a noninvasive neuroimaging modality, has predictive information in neural features associated with finger force control and can potentially contribute to the development of brain machine interfaces (BMI) for performing activities of daily living.

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“…This method could be used as a future diagnostic test in the follow-up of patients undergoing rehabilitation. It could also have potential applications in the fields of sports medicine [51].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

Kawala-Sterniuk

Podpora

Pelc

et al. 2020

Sensors

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This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

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Induced gamma band activity from EEG as a possible index of training-related brain plasticity in motor tasks

Cited by 9 publications

References 24 publications

BMI control of a third arm for multitasking

BMI control of a third arm for multitasking

Regression-based reconstruction of human grip force trajectories with noninvasive scalp electroencephalography

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

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