Decoding Object Weight from Electromyography during Human Grasping

Lashgari, Elnaz; Pouya, Amin Babaei; Maoz, Uri

doi:10.1101/2021.03.26.437230

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Specifically, the anterior deltoid and brachioradialis support reaching and lifting movements, whereas the common extensor digitorum, first dorsal interosseous, and the flexor digitorum profundus contribute mainly to the precision grip (Kapandji, 1980;Maier and Hepp-Reymond, 1995;Bonnefoy et al, 2009). Additionally, the target muscles have been identified to be informative as an indirect measure of grip force (Hoozemans and Van Dieen, 2005;Lashgari et al, 2021). The EMG data were used only in the multivariate analysis.…”

Section: Methodsmentioning

confidence: 99%

Grasping of Real-World Objects Is Not Biased by Ensemble Perception

et al. 2021

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The visual system is known to extract summary representations of visually similar objects which bias the perception of individual objects toward the ensemble average. Although vision plays a large role in guiding action, less is known about whether ensemble representation is informative for action. Motor behavior is tuned to the veridical dimensions of objects and generally considered resistant to perceptual biases. However, when the relevant grasp dimension is not available or is unconstrained, ensemble perception may be informative to behavior by providing gist information about surrounding objects. In the present study, we examined if summary representations of a surrounding ensemble display influenced grip aperture and orientation when participants reached-to-grasp a central circular target which had an explicit size but importantly no explicit orientation that the visuomotor system could selectively attend to. Maximum grip aperture and grip orientation were not biased by ensemble statistics during grasping, although participants were able to perceive and provide manual estimations of the average size and orientation of the ensemble display. Support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyography data of the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along unconstrained grasping dimensions, visually-guided behaviors toward real-world objects are not biased by ensemble processing.

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Section: Methodsmentioning

confidence: 99%

Grasping of Real-World Objects Is Not Biased by Ensemble Perception

et al. 2021

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“…In particular, it strongly enhances the scientific armamentarium used to investigate volition [3,4]. And, more specifically, decoding intention in real time would open the door to interesting experimental possibilities, such as interventions to facilitate or frustrate intentions [5][6][7], and intention-contingent stimulation [3]. Technological advances of recent decadessuch as untethered, wireless recording, machinelearning-based analysis, and real-time analysis of raw electroencephalography (EEG) signal have increased the interest in EEG based BCI approaches [8].…”

Section: Introductionmentioning

confidence: 98%

An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

et al. 2021

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Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing. Approach. To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain–computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community. Main results. Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning. Significance. Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.

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“…These studies have suggested that successful classification and pattern recognition of EMG signals require three main steps in the following order: (i) data preprocessing, (ii) feature extraction, and (iii) classification. Common EMG data preprcoessing steps include low- and high-pass filtering, whereas feature extraction is a method of finding intrinsic and meaningful information that may be latent in the EMG signal [ 12 , 13 ]. Over the past few decades, various manual EMG feature-extraction methods have explored in the time and/or frequency domains [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality reduction for classification of object weight from electromyography

Lashgari

Maoz

2021

PLoS ONE

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Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers’, who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object.

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Decoding Object Weight from Electromyography during Human Grasping

Cited by 3 publications

References 21 publications

Grasping of Real-World Objects Is Not Biased by Ensemble Perception

Grasping of Real-World Objects Is Not Biased by Ensemble Perception

An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

Dimensionality reduction for classification of object weight from electromyography

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