EEG sensor driven assistive device for elbow and finger rehabilitation using deep learning

Mukherjee, Prithwijit; Halder Roy, Anisha

doi:10.1016/j.eswa.2023.122954

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…Other works have demonstrated the behavior of cortical rhythms during the measurement of EEG signals and the use of upper limb robotic devices. For instance, the systems reported in [10,11] suggested the feasibility of BCIs to control finger movements in neurorehabilitation strategies. Moreover, other robotics-based BCIs were designed with the objective of rehabilitating spasticity over joints, such as the wrist, elbow, or shoulder [14,15].…”

Section: Discussionmentioning

confidence: 99%

“…In this context, active exoskeletons have been the most explored device with MI-based BCI applications because these frameworks can detect a motor intention of the subject and convert this task into a control command, allowing for an assisted movement [1]. For example, different exoskeletons have been used to control finger extension/flexion after detecting upper limb MI from EEG [10,11]. Alternative systems have served as end-effectors to produce movements of flexion/extension in the upper limbs [12].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks

Guerrero-Mendez,

Blanco-Diaz,

Rivera-Flor

et al. 2024

NeuroSci

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Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain–computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high—85 rpm and low—30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly (p< 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks

Guerrero-Mendez,

Blanco-Diaz,

Rivera-Flor

et al. 2024

NeuroSci

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show abstract

“…Affective computing is an umbrella term for human emotion, sentiment, and emotion recognition. As emotion affects human daily behaviors and cognitive activities, emotion recognition plays a crucial role in research fields such as artificial intelligence, medical and health [1][2][3][4][5][6], and brain-computer interfaces (BCI). Generally, both nonphysiological and physiological signals can be used to recognize human emotion [7].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Interaction in Multiscale Multichannel EEG Signals for Emotion Recognition

Guo,

Zhang,

Fan

et al. 2024

Mathematics

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Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various time scales and on different channels. We propose a cascade scale-aware adaptive graph convolutional network and cross-EEG transformer (SAG-CET) to explore the comprehensive interaction between multiscale and multichannel EEG signals with two novel ideas. First, to model the relationship of multichannel EEG signals and enhance signal representation ability, the multiscale EEG signals are fed into a scale-aware adaptive graph convolutional network (SAG) before the CET model. Second, the cross-EEG transformer (CET), is used to explicitly capture multiscale features as well as their correlations. The CET consists of two self-attention encoders for gathering features from long-short time series and a cross-attention module to integrate multiscale class tokens. Our experiments show that CET significantly outperforms a vanilla unitary transformer, and the SAG module brings visible gains. Our methods also outperform state-of-the-art methods in subject-dependent tasks with 98.89%/98.92% in accuracy for valence/arousal on DEAP and 99.08%/99.21% on DREAMER.

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EEG sensor driven assistive device for elbow and finger rehabilitation using deep learning

Cited by 2 publications

References 38 publications

Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks

Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks

A Comprehensive Interaction in Multiscale Multichannel EEG Signals for Emotion Recognition

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