Analysis of electroencephalography alteration during sustained cycling exercise using power spectrum and fuzzy entropy Electroencephalogram (EEG) is a common tool to study the changes in brain activity during exercise. In this study, we used recorded Electrocardiography (ECG) to define an averagetomaximal heart rate ratio (AMHRR) as a gauge of relative exercise workload, and explored how the increase of AMHRR affected the brain activity. The EEG signals were recorded from fortyfour healthy subjects on four scalp sites, including C1, C2, P1, and P2 during nine minutes nonstop cycling exercise. The relationships between AMHRR and EEG power spectral or EEG fuzzy entropy (FuzzyEn) were established. Results of EEG power spectral and FuzzyEn displayed similar increasing patterns with AMHRR at all electrodes. Compared with EEG power spectral, the FuzzyEn has better specificity in selecting effective frequency bands, namely theta, alpha and beta bands. The FuzzyEn method can be applied in a wearable device for human machine interface (HMI) in monitoring the EEG during exercise in the future.

EEG Events Patterns Recognition for Robotics Reasoning and Decision Enhancement Electroencephalography (EEG) based robotics systems, are receiving substantial attentions due to the mass amount of information hidden with human brainwaves. Classification and recognition of electroencephalography of human brainwaves for eye thoughts and movements, is presented within this article. The adopted technique is based on the use of Random Forests (RF) learning paradigm, to classify dissimilar events features, and different human thoughts while looking at images and creating thoughts about the observed images. Features extraction is an essentials stage within this hierarchy, therefore two classes of features extractions were adopted and combined, this include the time domain (TD), and the spectral properties (FD) features. Outcomes of the EEGrelated patterns recognition are hence fed into a higher level of decisionmaking paradigm using fuzzybased decision system, in such a way to acquire a robotic system diverse robotic tasks and movements. Development of Random Forest classification for the EEGRobotic system, has shown to be an effective method for modem robotics uses and applications.