Real-time decoding of 5 finger movements from 2 EMG channels for mixed reality human-computer interaction

Ej, McDermott; Zwiener, Thimm; Ziemann, Ulf; Zrenner, Christoph

doi:10.1101/2021.09.28.462120

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…For each condition separately, time frequency analysis (TFA) was performed using FFT in the range of 3-40 Hz across each participant, task, and individual trial. We selected 5 different frequency bands: theta (3-7 Hz), alpha (7-13 Hz), low beta (13-16 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and gamma (26-40 Hz), and then averaged across each of these ranges for all trials within a task for each participant. In the brain and artifact conditions, this trialaveraged TF data was then binned to create 10 time bins for each of the 5 frequency bands for each of the 10 components over each task and each participant, creating 500 (10 × 5 × 10) features for each participant.…”

Section: The Time-frequency Approachmentioning

confidence: 99%

“…For each condition separately, the EEG signals were bandpass filtered in each of six frequency bands: theta (3-7 Hz), alpha (7-13 Hz), low beta (13-16 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), gamma (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), and 'all' (3-40 Hz). The channel-specific bandpower for every single trial was calculated for the band-passed data using the equation:…”

Section: The Bandpower Approachmentioning

confidence: 99%

“…We chose a VR-based paradigm because the combination of EEG and VR allows for a future “closed-loop” application where the EEG signal influences the VR paradigm in real time to optimize treatment outcome. Additionally, VR paradigms are also reported to be more engaging, motivating, and fun than their traditional therapeutic counterparts [ 18 , 19 ], and we have successfully created a similar closed-loop system using electromyography (EMG) [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Artifacts in EEG-Based BCI Therapies: Friend or Foe?

McDermott

Raggam

Kirsch

et al. 2021

Sensors

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EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

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Section: The Time-frequency Approachmentioning

confidence: 99%

Section: The Bandpower Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artifacts in EEG-Based BCI Therapies: Friend or Foe?

McDermott

Raggam

Kirsch

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Artifacts in EEG-based BCI therapies: friend or foe?

McDermott

Raggam

Kirsch

et al. 2021

Preprint

Self Cite

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EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain-states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

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An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.

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Real-time decoding of 5 finger movements from 2 EMG channels for mixed reality human-computer interaction

Cited by 3 publications

References 29 publications

Artifacts in EEG-Based BCI Therapies: Friend or Foe?

Artifacts in EEG-Based BCI Therapies: Friend or Foe?

Artifacts in EEG-based BCI therapies: friend or foe?

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

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