Examination of Prefrontal Cortex Activity After EEG-Neurofeedback Stimulation in Overweight Cases

Food intake regulation is considered the key to weight control and overweight prevention. The brain activity in PreFrontal Cortex (PFC) plays a role in food intake behaviors. Most of the previous studies were aimed to PFC stimulation in overweight cases to modify the food intake behaviors. The EEG-neurofeedback is one of  the brain stimulation techniques; therefore, this study aims to find the effect of  EEG-NF stimulation on PFC function by EEG features analysis. For the purpose of analysis, the theta\beta ratio was extracted from ten healthy overweight participators in this study. All participants were divided into two groups, experimental group and control group with two phase-terms, pre and post-stimulation phase. The experiments were run using EEG-NF device. The results in this study indicate that success of EEG-Neurofeedback in PFC stimulation of overweight cases may have an influence on changing the food intake behavior

Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study