BCI based on pedal end-effector triggered through pedaling imagery to promote excitability over the feet motor area

Cardoso, Vivianne; Delisle-Rodríguez, Denis; Romero-Laiseca, Maria Alejandra; Loterio, Flávia Aparecida; Gurve, Dharmendra; Floriano, Alan; Krishnan, Sridhar; Frizera, Anselmo; Filho, Teodiano Freire Bastos

doi:10.1007/s42600-021-00196-7

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…Considering Table 1 , traditional motor imagery, which consists of MI related to distinct limbs, such as movement in the hands, feet (or legs), and tongue, was the most popular paradigm. This paradigm presented a particularly intuitive method of control for exoskeletons [ 34 , 35 ], in which the user can imagine walking or sitting [ 35 ]; rehabilitative pedaling machines, in which the user can imagine the pedaling motion [ 36 ]; and robotic hands, in which the hand can mimic imagined flexion or extension [ 37 , 38 ]. Many systems depended wholly or in part on classifying left- and right-hand MI, possibly because these produced distinct pattens in the right and left hemispheres, respectively, that can be accurately classified [ 15 ].…”

Section: Bcis In the Physical World: Applications And Paradigmsmentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

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Section: Bcis In the Physical World: Applications And Paradigmsmentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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“…McFarland, Daly, Boulay, & Parvaz, 2017). The modulation of these sensorimotor rhythms has been employed to classify imagined and executed body movements (Cardoso et al, 2022;Duann & Chiou, 2016;Krishnan & Soman, 2021). In addition, SMR has also shown the potential to categorize gross lower limb tasks, including differentiation of right and left leg motor imagery (Batula, Mark, Kim, & Ayaz, 2017;Bian et al, 2023) and detection of lower-limb movement intention (L. Gu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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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“…However, most people affected by pathologies that limit their motor functionality have no previous experience with BCI systems. For this reason, it is necessary to implement extensive protocols that lead to calibration processes between different days to generate user experience [15,16]. Nevertheless, even after training, users may not be suitable for controlling robotic devices using BCI, and one of the main causes is the lack of skill or experience in imagining movements [15].…”

Section: Introductionmentioning

confidence: 99%

“…Studies have implemented BCI systems in two sessions, one for data acquisition (offline) and the other for evaluation (online) of the methods, because offline data acquisition generates some experience for the subject [15,16]. However, the data recorded in preevaluation (offline) sessions are regularly used to train the methods, which generates uncertainty when the system is implemented in the online evaluation phase.…”

Section: Introductionmentioning

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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BCI based on pedal end-effector triggered through pedaling imagery to promote excitability over the feet motor area

Cited by 3 publications

References 39 publications

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

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

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