Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

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.

On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson's disease.

Parkinson's disease (PD), whose cardinal signs are tremor, rigidity, bradykinesia, and postural instability, gradually reduces the quality of life of the patient, making early diagnosis and follow-up of the disorder essential. This study aims to contribute to the objective evaluation of tremor in PD by introducing and assessing histograms of oriented gradients (HOG) to the analysis of handwriting sinusoidal and spiral patterns. These patterns were digitized and collected from handwritten drawings of people with PD (n = 20) and control healthy individuals (n = 20). The HOG descriptor was employed to represent relevant information from the data classified by three distinct machine-learning methods (random forest, k-nearest neighbor, support vector machine) and a deep learning method (convolutional neural network) to identify tremor in participants with PD automatically. The HOG descriptor allowed for the highest discriminating rates (accuracy 83.1%, sensitivity 85.4%, specificity 80.8%, area under the curve 91%) on the test set of sinusoidal patterns by using the one-dimensional convolutional neural network. In addition, ANOVA and Tukey analysis showed that the sinusoidal drawing is more appropriate than the spiral pattern, which is the most common drawing used for tremor detection. This research introduces a novel and alternative way of quantifying and evaluating tremor by means of handwritten drawings.

Measurement of post-stroke spasticity based on tonic stretch reflex threshold: implications of stretch velocity for clinical practice.

PURPOSE: The most commonly used method for the clinical evaluation of spasticity is the modified Ashworth scale (MAS), which is subjective. In this regard, the spasticity assessment through the tonic stretch reflex threshold, which is an objective method, has emerged as an alternative. It is based on the value of the dynamic stretch reflex threshold, which is measured at different stretch velocities. However, by this definition, it is not possible to define the speed at which passive stretches should be performed during evaluation. OBJECTIVE: This study aimed to evaluate whether the speed-variation sequence used to acquire the dynamic stretch reflex threshold influences the tonic stretch reflex threshold (TSRT) and, consequently, the estimation of spasticity by this method. METHODS: Three forms of stretching-variation speed were adopted, i.e., increasing, decreasing, and randomised. The study was performed using 10 post-stroke patients. RESULTS AND CONCLUSIONS: The results showed that the stretch protocols were not all the same and that the method of increasing was most suitable for performing manual passive stretches to evaluate TSRT in these patients. Another analysis was the correlation between MAS and tonic stretch reflex threshold; a weak correlation was observed between the increasing and decreasing methods, and moderate correlation was observed between the random methods. Implications for Rehabilitation We demonstrated that the protocol of execution of passive stretches influences in the measurement of the tonic stretch reflex threshold (TSRT). We recommend the method of increasing velocity for performing manual passive stretches. We also build software with a reliable biological data acquisition system, which makes acquisition and processing of data in real time. In this way, the TSRT is a promising quantitative measure to assess post-stroke spasticity, calculated automatically. We also we provided the use of portable instruments to facilitate the assessment of spasticity in clinical practice.