The objective measurement of subjective, multi-dimensionally experienced pain is a problem for which there has not been an adequate solution. Although verbal methods (e.g., pain scales and questionnaires) are commonly used to measure clinical pain, they tend to lack objectivity, reliability, or validity when applied to mentally impaired individuals. Biopotential and behavioral parameters may represent a solution. Such coding systems already exist, but they are either very costly or time-consuming or have not been sufficiently evaluated. In this context, we collected a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. One hundred thirty-five features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, and variability. The following features were chosen as the most selective: (1) electromyography corrugator peak to peak, (2) corrugator shannon entropy, and (3) heart rate variability slope RR. Individual-specific calibration allows the adjustment of feature patterns, resulting in significantly more accurate pain detection rates. The objective measurement of pain in patients will provide valuable information for the clinical team, which may aid the objective assessment of treatment (e.g., effectiveness of drugs for pain reduction, information on surgical indication, and quality of care provided to patients).

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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.

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.