Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement

Motor imagery-based brain–computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models (p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM

kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances

The Skeletal Muscle Anabolic Response to Plant- versus Animal-Based Protein Consumption

Clinical and consumer market interest is increasingly directed toward the use of plant-based proteins as dietary components aimed at preserving or increasing skeletal muscle mass. However, recent evidence suggests that the ingestion of the plant-based proteins in soy and wheat results in a lower muscle protein synthetic response when compared with several animal-based proteins. The possible lower anabolic properties of plant-based protein sources may be attributed to the lower digestibility of plant-based sources, in addition to greater splanchnic extraction and subsequent urea synthesis of plant protein–derived amino acids compared with animal-based proteins. The latter may be related to the relative lack of specific essential amino acids in plant- as opposed to animal-based proteins. Furthermore, most plant proteins have a relatively low leucine content, which may further reduce their anabolic properties when compared with animal proteins. However, few studies have actually assessed the postprandial muscle protein synthetic response to the ingestion of plant proteins, with soy and wheat protein being the primary sources studied. Despite the proposed lower anabolic properties of plant vs. animal proteins, various strategies may be applied to augment the anabolic properties of plant proteins. These may include the following: 1 ) fortification of plant-based protein sources with the amino acids methionine, lysine, and/or leucine; 2 ) selective breeding of plant sources to improve amino acid profiles; 3 ) consumption of greater amounts of plant-based protein sources; or 4 ) ingesting multiple protein sources to provide a more balanced amino acid profile. However, the efficacy of such dietary strategies on postprandial muscle protein synthesis remains to be studied. Future research comparing the anabolic properties of a variety of plant-based proteins should define the preferred protein sources to be used in nutritional interventions to support skeletal muscle mass gain or maintenance in both healthy and clinical populations