Coherence-based connectivity analysis of EEG and EMG signals during reach-to-grasp movement involving two weights

Guerrero-Méndez, Cristian David; Ruiz-Olaya, Andres F.

doi:10.1080/2326263x.2022.2029308

Cited by 17 publications

(21 citation statements)

References 40 publications

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“…Using upper and lower body segments, standing and sitting give rise to complex and dynamic tasks. However, the execution of this type of task requires the activation of deeper areas located in the motor cortex of the brain to synchronously command and co-activate the muscles located in the lower limbs, which are further away from the CNS than the location in the upper limbs (Guerrero-Mendez & Ruiz-Olaya, 2022;Pfurtscheller & Neuper, 2001). Considering this, MI and ME discrimination for standing and sitting is not an easy task; therefore, the differences in power spectral modulation of MI and ME allowing broaden the knowledge of the CNS in order to understand the execution of these paradigms towards the creation of new rehabilitation strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, artifact detection was performed using thresholds on channels C3, Cz, C4 for the MI and ME experiments. Accordingly, the MI signals had to remain in the range ±80 µV and the ME signals in the range ±400 µV otherwise, the trial was rejected for all three channels (Dimigen, 2020;Guerrero-Mendez & Ruiz-Olaya, 2022). Additionally, if trial rejection Fig.…”

Section: Preprocessingmentioning

confidence: 99%

“…Upper-limb MI has been studied extensively. Different power effects have been reported mainly using Events Related to Synchronization (ERS, increased amplitude and power of the EEG signal) or Desynchronization (ERD, decreased amplitude and power of the EEG signal) (Guerrero-Mendez & Ruiz-Olaya, 2022;Padfield et al, 2019;Pfurtscheller & Neuper, 2001). These effects focus on the control-laterality and ipsilaterally of the imagination of movements with the skilled hand and the hemisphere of the motor cortex presenting ERDS recorded mainly by C3 and C4 EEG channels (Guerrero-Mendez & Ruiz-Olaya, 2022;Padfield et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Different power effects have been reported mainly using Events Related to Synchronization (ERS, increased amplitude and power of the EEG signal) or Desynchronization (ERD, decreased amplitude and power of the EEG signal) (Guerrero-Mendez & Ruiz-Olaya, 2022;Padfield et al, 2019;Pfurtscheller & Neuper, 2001). These effects focus on the control-laterality and ipsilaterally of the imagination of movements with the skilled hand and the hemisphere of the motor cortex presenting ERDS recorded mainly by C3 and C4 EEG channels (Guerrero-Mendez & Ruiz-Olaya, 2022;Padfield et al, 2019). In contrast, lower-limb MI has been poorly studied in the literature, especially in the performance of Activities of Daily Living (ADL); only in the last few years has there been interest in studies on the MI and Motor Execution (ME) of walking (R. Ma, Chen, Jiang, & Zhang, 2023;Ortiz et al, 2023) or pedaling (Blanco-Díaz et al, 2023) tasks.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Motor Imagery and Execution of Lower Limb Tasks: EEG Analysis during Transitions from Sitting to Standing

Triana-Guzman,

Guerrero-Mendez,

Moreno-Arevalo

et al. 2024

Preprint

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Purpose: Motor Imagery (MI) and Motor Execution (ME) of the upper limbs have been extensively studied in the literature, whereas research on MI and ME of lower limb movements remains comparatively scarce, especially in tasks related to those involved in Activities of Daily Living (ADLs), such as standing and sitting. Therefore, in this study, the temporal and frequency features of EEG signals recorded by healthy subjects with no previous experience in recording Electroencephalography (EEG) signals were analyzed. Methods: The roles of MI and ME were investigated in a paradigm based on standing and sitting transitions tasks guided by visual cues presented on a screen, by randomizing MI and ME. Sensorimotor Rhythms (SMR) were analyzed using power spectral modulation and Event Related Synchronization/Desynchronization (ERDS) features. Results: As a result, Alpha waves (7-13 Hz) are more discriminant between MI and ME than Beta waves (14-35 Hz). Significant differences were observed between the standing and sitting MI and ME groups. In addition, the sitting task was found to have higher power spectral than the standing task. Conclusion: EEG analyses during the evaluation of complex and natural tasks, such as standing and sitting in inexperienced subjects, could serve as a basis for more effective interventions. In addition, novel strategies for motor rehabilitation could be applied, as well as increasing the understanding of the Central Nervous System (CNS) related to standing and sitting transitions tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Motor Imagery and Execution of Lower Limb Tasks: EEG Analysis during Transitions from Sitting to Standing

Triana-Guzman,

Guerrero-Mendez,

Moreno-Arevalo

et al. 2024

Preprint

Self Cite

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“…Trial rejection was performed according to the threshold criteria, in which the MI segments should be within the range of ±100 μV. Subjects with a removal greater than 10% of the total number of trials were discarded [27]. This was performed for each EEG channel.…”

Section: Signal Pre-processingmentioning

confidence: 99%

EEG motor imagery classification using deep learning approaches in naïve BCI users

Guerrero-Méndez

Blanco-Díaz

Ruiz-Olaya

et al. 2023

Biomed. Phys. Eng. Express

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Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p<0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.

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Wavelet Coherence Corticomuscular Analysis During Tasks Involved in Object Manipulation

Guerrero-Mendez,

Blanco-Diaz,

Bastos-Filho

2024

IFMBE Proceedings

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Coherence-based connectivity analysis of EEG and EMG signals during reach-to-grasp movement involving two weights

Cited by 17 publications

References 40 publications

Exploring Motor Imagery and Execution of Lower Limb Tasks: EEG Analysis during Transitions from Sitting to Standing

Exploring Motor Imagery and Execution of Lower Limb Tasks: EEG Analysis during Transitions from Sitting to Standing

EEG motor imagery classification using deep learning approaches in naïve BCI users

Wavelet Coherence Corticomuscular Analysis During Tasks Involved in Object Manipulation

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