Decoding the evolving grasping gesture from electroencephalographic (EEG) activity

Agashe, Harshavardhan A.; Contreras-Vidal, José L.

doi:10.1109/embc.2013.6610817

Cited by 17 publications

(11 citation statements)

References 11 publications

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“…In addition, an indirect measurement method typified by computer vision has excellent recognition performance with the help of deep learning [2] , but the recognition accuracy is affected by intensity of surrounding light, image background, and obstacle occlusion. Recently, bio-signal based hand recognition has become a hotspot [3][4][5] , which has the advantages of comfortable wearing, unrestricted hand shape and outdoor usage. Since the motion of the hand and wrist is mainly innervated by the forearm muscles, one of the mainstream research directions is to extract potential motion intentions from the forearm muscles signals.…”

Section: Introductionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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In this paper an automated data labeling (ADL) neural network was proposed to streamline dataset collecting for real-time predicting the continuous motion of hand and wrist, these gestures are only decoded from a surface electromyography (sEMG) array of eight channels. Unlike collecting both the bio-signals and hand motion signals as samples and labels in supervised learning, this algorithm only collects the unlabeled sEMG into an unsupervised neural network, in which the hand motion labels are auto-generated. The coefficient of determination (r2) for three DOFs, i.e. wrist flex/extension, wrist pro/supination, hand open/close, was 0.86, 0.89 and 0.87 respectively. The comparison between real motion labels and auto-generated labels shows that the latter has earlier response than former. The results of Fitts' law test indicate that ADL has capability of controlling multi-DOFs simultaneously even though the training set only contains sEMG data from single DOF gesture. Moreover, no more hand motion measurement needed which greatly helps upper-limb amputee imagine the gesture of residual limb to control a dexterous prosthesis.

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Section: Introductionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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“…The electroencephalogram (EEG) offers the potential to examine cognitive effort with precise temporal resolution and freedom of movement during data collection, facilitating adaptability to clinical, operational, or real-world settings [24] , [25] , [26] , [27] . Remarkably, although efforts to use measures of cortical dynamics such as EEG are increasingly abundant in the literature for control of assistive devices [28] , [29] , [30] , [31] , [32] , [33] , [34] , EEG measures of cognitive workload for evaluation of new HMI technologies have not been adapted and applied to this field.…”

Section: Introductionmentioning

confidence: 99%

A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

et al. 2014

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Common goals in the development of human-machine interface (HMI) technology are to reduce cognitive workload and increase function. However, objective and quantitative outcome measures assessing cognitive workload have not been standardized for HMI research. The present study examines the efficacy of a simple event-related potential (ERP) measure of cortical effort during myoelectric control of a virtual limb for use as an outcome tool. Participants trained and tested on two methods of control, direct control (DC) and pattern recognition control (PRC), while electroencephalographic (EEG) activity was recorded. Eighteen healthy participants with intact limbs were tested using DC and PRC under three conditions: passive viewing, easy, and hard. Novel auditory probes were presented at random intervals during testing, and significant task-difficulty effects were observed in the P200, P300, and a late positive potential (LPP), supporting the efficacy of ERPs as a cognitive workload measure in HMI tasks. LPP amplitude distinguished DC from PRC in the hard condition with higher amplitude in PRC, consistent with lower cognitive workload in PRC relative to DC for complex movements. Participants completed trials faster in the easy condition using DC relative to PRC, but completed trials more slowly using DC relative to PRC in the hard condition. The results provide promising support for ERPs as an outcome measure for cognitive workload in HMI research such as prosthetics, exoskeletons, and other assistive devices, and can be used to evaluate and guide new technologies for more intuitive HMI control.

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“…Recently, it was demonstrated that MRCPs associated with movements performed with different levels of force and speed of the same body part (wrist and foot movements) could be decoded from the EEG using only information prior to the onset of the movement [13,14,21]. Also, different movement types have been classified such as hand grasping, opening and reaching [1,2,4], movement direction and kinematics (see [19] for a recent review), wrist movements [12,[40][41][42], shoulder and elbow movements [8,[47][48][49] and finger movements [26,27,44].…”

Section: Introductionmentioning

confidence: 99%

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Jochumsen

Niazi

Dremstrup

et al. 2015

Med Biol Eng Comput

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Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention is crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior the movement onset. Fourteen healthy subjects performed the three grasps 100 times while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA).The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79±8%) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features; 76±9% (2-class) and 63±10% (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset; which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.

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Decoding the evolving grasping gesture from electroencephalographic (EEG) activity

Cited by 17 publications

References 11 publications

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

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