Mouse emulation based on facial electromyogram

Andrade, Adriano O.; Pereira, Adriano Alves; Pinheiro, Carlos G.; Kyberd, Peter

doi:10.1016/j.bspc.2012.09.001

Cited by 11 publications

(11 citation statements)

References 22 publications

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“…Data were collected from ten healthy individuals during the execution of successive facial muscular contractions to characterize the EEG signal contamination by facial muscular activity. This experimental protocol was based on previous published work [51] reporting the implementation of a facial EMG interface and motor learning assessment.…”

Section: Database and Participantsmentioning

confidence: 99%

“…However, the main limitations of this class of noise removal technique is that its performance is low for signal-to-noise ratios below -10 dB [18,30,50], which is typical in EEG contaminated by facial EMG. This is important when there is a need to monitor brain activity during human computer interaction based on facial electromyography [51].…”

Section: Introductionmentioning

confidence: 99%

“…The results of the proposed method, so-called EMDRLS, were compared with those obtained from traditional methods such as the wavelet transform, Wiener filtering, EMD and hybrid filtering. The experimental protocol adopted in this study is based on the practical need of assessing brain activity for motor learning quantification during the interaction with a myoelectric interface [51]. Figure 1 presents a visual inspection of a typical EEG epoch from channel F7-F3 with EMG contamination from masseter muscular contraction.…”

Section: Introductionmentioning

confidence: 99%

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Single channel approach for filtering EEG signals strongly contaminated with facial EMG

Silva

Queiroz

Walter

et al. 2020

Preprint

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Background Eliminating facial electromyography (EMG) from the electroencephalogram (EEG) is essential for the accuracy of applications such as brain computer interfaces (BCIs) and quantification of brain functionality. Although it is possible to find several studies that address EEG filtering, there is lack of researches that improve the filtering of EEG strongly corrupted by EMG signals with single-channel approaches, which are necessary in situations in which the number of available channels is reduced for the application of filtering methods based on multichannel techniques. In this context, this research proposes an EEG denoising method for filtering EMG from the masseter and frontal. This method, so-called EMDRLS, combines the use of Empirical Mode Decomposition (EMD) and a Recursive Least Square (RLS) filter to attenuate facial EMG noise from EEG. The results were compared with those obtained from Wavelet, EMD, Wiener and Wavelet-RLS (WRLS) filters. Besides the visual inspection of the resultant waveform of filtered signals, the following objective metrics were employed to contrast the performance of the filtering methods: (i) the signal-noise ratio (SNR) of the contaminated signal, (ii) the root mean square error (RMSE) between the power spectrum of artifact free and filtered EEG epochs, (iii) the spectral preservation of brain rhythms (i.e., delta, theta, alpha, beta, and gamma rhythms) of filtered signals.Results The EMDRLS method yielded filtered EEG signals with SNR varying from 0 to 10 dB for EEG signals with SNR below -10dB. The Spearman’s correlation coefficient estimated between the SNR of filtered and corrupted signals was below 0.04, suggesting, in the evaluated conditions, the independence of the EMDRLS filtering performance to the SNR of noisy signals. The technique also improved the RMSE between the power spectrum of artifact free and filtered EEG epochs by a factor of 27 (from 5.429 to 0.197) in the most corrupted EEG channels with the masseter muscle contraction. The Kruskal-Wallis test and the Tukey-Kramer post-hoc test (p < 0.05) confirmed the preservation of all brain rhythms given by EEG signals filtered with the EMDRLS method.Conclusions The results showed that the single-channel EMDRLS method can be applied to highly contaminated EEG signals by facial EMG signal with performance superior to that of the compared methods. The method can be applied for the offline filtering of EEG signals contaminated by facial EMG.

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Section: Database and Participantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single channel approach for filtering EEG signals strongly contaminated with facial EMG

Silva

Queiroz

Walter

et al. 2020

Preprint

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“…Using surface electromyography (sEMG), the electrical activity of face and neck musculature can be detected by electrodes placed on the surface of the skin. Individuals can then volitionally activate certain muscles to control a human-machine interface (HMI), such as a computer pointing device [1, 2, 4–7, 11] or even a power wheelchair [9–11]. Unlike the use of eye-tracking systems for computer access, sEMG is insensitive to lighting conditions and is suitable for all types of skin with proper skin preparation [12–14].…”

Section: Introductionmentioning

confidence: 99%

“…Most notably, direct control via facial sEMG has been shown to enable 360° cursor movements with four direction-based sEMG sources [2], while pattern recognition techniques would only enable cursor movement in the four cardinal directions with the same number of sensors. Yet, previous studies using sEMG cursor control (e.g., [1–3, 5, 7, 8]) have not evaluated small adjustments in sensor configuration, but instead relied on trained operators and trial-and-error to arrive at an “optimal” sensor configuration. Therefore, we investigated quantitative sEMG signal features extracted from a short calibration process in order to determine if user control performance within an HMI could be quickly and accurately predicted.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human–Machine Interface Control

Vojtech

Cler

Stepp

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.

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FPGA-based communication interface for persons with motor neuron diseases

Hajduk

2016

Biomedical Signal Processing and Control

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Mouse emulation based on facial electromyogram

Cited by 11 publications

References 22 publications

Single channel approach for filtering EEG signals strongly contaminated with facial EMG

Single channel approach for filtering EEG signals strongly contaminated with facial EMG

Prediction of Optimal Facial Electromyographic Sensor Configurations for Human–Machine Interface Control

FPGA-based communication interface for persons with motor neuron diseases

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