Decoding natural reach-and-grasp actions from human EEG

Schwarz, Andreas; Ofner, Patrick; Pereira, Joana; Sburlea, Andreea Ioana; Müller-Putz, Gernot

doi:10.1088/1741-2552/aa8911

Cited by 123 publications

(154 citation statements)

References 62 publications

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“…The authors state that one movement can be classified from another with 55% classification accuracy. This research group achieved the classification accuracy of 93% while classifying grasping actions of hand from its rest state compared to the classification results stated by Jochumsen et al in [21] [23].…”

Section: Introductionmentioning

confidence: 58%

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Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

Butt¹,

Naghdy²,

Naghdy³

et al. 2019

Adv. sci. technol. eng. syst. j.

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Improving the hand motor skills in post-stroke patients through rehabilitation based on movement intention derived signals from the brain in conjunction with robot-assistive technologies are explored. The experimental work is conducted using Electroencephalogram based Brain-Computer Interface (EEG-BCI) system and the AMADEO hand rehabilitation robotic device. Two protocols using visual-cues and then using a 2-Dimensional (2D) interactive game is presented on a computer screen to healthy subjects as well as post-stroke patients performing the hand movements. The movement intention signals during hand movement are detected through the Support Vector Machine (SVM) classifier. The intent signals produced at six distinct electrodes are investigated to determine electrodes contributing most to the SVM classifier's performance. Overall, the game protocol shows better classification results for both healthy and stroke patients compared to the visual-cues protocol. FC3 is found to be the most consistent electrode site for the detection of the motor intention of the hand for both protocols. In the experimental work, average classification accuracy for the visual-cues protocol of 67.56% for healthy subjects and 56.24% for stroke patients were obtained. For the game protocol, the classifier accuracy produced for healthy participants was 79.7% and for the post-stroke patients was 66.64%. The results confirm that the intention signal is more pronounced during more engaging activities, such as playing games, for both healthy and stroke subjects. Therefore, the effectiveness of rehabilitation therapy for post-stroke patients could be significantly enhanced using interactive and engaging exercise protocols.

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Section: Introductionmentioning

confidence: 58%

“…Many studies demonstrate applications of EEG-BCI systems to detect movement intention for various upper-limbs, such as movements of the arm [11][12][13][14][15], elbow [16], [17] and wrist [18] and hand [19][20][21][22][23] for post-stroke rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

Butt¹,

Naghdy²,

Naghdy³

et al. 2019

Adv. sci. technol. eng. syst. j.

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“…However, explorations of kinetic information as a control signal for iBCIs have only just begun. The majority have characterized neural modulation to executed kinetic tasks in primates and ablebodied humans (Filimon et al, 2007;Moritz et al, 2008;Pohlmeyer et al, 2009;Ethier et al, 2012;Flint et al, 2012;Flint et al, 2014;Flint et al, 2017;Schwarz et al, 2018). Small subsets of M1 neurons have been used to command muscle activations through FES to restore one-dimensional wrist control and whole-hand grasping in non-human primates with temporary motor paralysis (Moritz et al, 2008;Pohlmeyer et al, 2009;Ethier et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

Rastogi

Willett

Abreu

et al. 2020

Preprint

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Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: 1) Force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); 2) Grasp type affected force representation within multi-unit neural features and offline force classification accuracy; and 3) Grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type. Significance StatementIntracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to potentially restore hand grasping and object interaction in people with tetraplegia. This study is among the first to quantify the degree to which hand grasp affects force-related -or kineticneural activity and decoding performance in individuals with tetraplegia. The study results enhance our overall understanding of how the brain encodes kinetic parameters across varying kinematic behaviors --and in particular, the degree to which these parameters have independent versus interacting neural representations. Such investigations are a critical first step to incorporating force control into human-operated iBCI systems, which would move the technology towards restoring more functional and naturalistic tasks.

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“…The stages of the classification of the limb movement using EEG signal is similar to that using EMG signal, but with more rich features [5]. Special for hand movement, the focus of the movement classification is to differentiate the left or right-hand movement [4] [6] [7]. In fact, the hand movement includes finger movement.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Anam¹,

Nuh²,

Al-Jumaily³

2019

EECSI

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The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI), especially for finger movement detection. Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to achieve better performance.

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Decoding natural reach-and-grasp actions from human EEG

Cited by 123 publications

References 62 publications

Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

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