A labview based stand alone system to monitor the eeg and emg of video gaming kids

Abstract

In the recent past negative aspects of video gaming, related brain abnormalities, and screening methodologies has been considered as one of the significant area of research. In this research EEG signal has been taken for parametric analysis whereas EMG analysis is taken in few aspect which has been discussed in the methodologies. During this detection process, different steps are applied to detect the brain abnormalities with the effective manner by reducing the error rate. In this research the problem is overcome by applying the different classification methods. Initially the video game playing kids abnormality has been recognized by applying the Adaptive Neuro Fuzzy Radial Neural Network (ANFRNN) approach. This proposed method obtains the bio-signals from labview stand-alone environment which consists of several noises that has been removed by analyzing each frequency and the different key point s related features are extracted along with statistical features which are derived. The extracted features are examined by comparison field, recognition field along with this vigilance parameter and reset module in neural network which successfully classifies the abnormal feature set by using the fuzzy membership value. Furthermore, Ant colony with Global optimization and Boosting Based Learning Vector Quantization Neural Networks (BLVQNN) approach is used for recognizing the brain abnormality recognition process. Initially, bio-signals are recorded with the help of LabVIEW standalone environment, noise present in the signal is eliminated by constructing the matrix by multi-linear principal component analysis method. newline