Quantifying the role of motor imagery in brain-machine interfaces

Marchesotti, Silvia; Bassolino, Michela; Serino, Andrea; Bleuler, Hannes; Blanke, Olaf

doi:10.1038/srep24076

Cited by 91 publications

(94 citation statements)

References 60 publications

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“…For the present study, we, cannot follow up on this possibility, given that we did not include an MI ability or severity questionnaire. It should, however, be noted that former studies found no (Rimbert et al, 2019) or rather moderate (Vuckovic and Osuagwu, 2013;Marchesotti et al, 2016) relations between subjective MI ability measures and BCI literacy.…”

Section: Study Limitationsmentioning

confidence: 72%

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Spychała

Debener

Bongartz

et al. 2020

Front. Hum. Neurosci.

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Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.

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Section: Study Limitationsmentioning

confidence: 72%

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Spychała

Debener

Bongartz

et al. 2020

Front. Hum. Neurosci.

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“…The use of objective questionnaires has been proposed in order to determine whether a person will be able to use motor imagery effectively [42]. Miller et al found that activation in M1 during motor imagery, measured using electrocorticography (ECoG), can be increased via training with feedback and that in some cases it can exceed the original motor execution levels [28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS

Batula

Mark

Kim

et al. 2017

Computational Intelligence and Neuroscience

126

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Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.

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“…Initially, we discuss the classifier performance of the computed PSD vectors of contribution, ρ r,∆ f . In each one of the tested scenarios for spectral bandwidths of interest, parameter tuning is carried out to achieve the maximum accuracy within the MI interval [3][4][5], s. As seen in In terms of the tuned CNN parameters, their values averaged across the subject set show that the training scenario achieving the best accuracy (µ∪β low ∪β med ∪β high ) demands from the PSD-based vectors more hidden units hu than in the case of CWT planes. A similar situation holds in the scenario µ∪β that also performs high accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…One of the main challenges in implementing MI practice is to recognize and identify the imagined actions since EEG signals have substantial intra and inter subject variability [5].…”

Section: Introductionmentioning

confidence: 99%

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Collazos-Huertas

Meza

Domínguez

2020

Preprint

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Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

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Quantifying the role of motor imagery in brain-machine interfaces

Cited by 91 publications

References 60 publications

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

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