Innovative approach in analysis of EEG and EMG signals — Comparision of the two novel methods

In this paper comparison of the two innovative signal processing methods for analysis of both EEG and EMG biomedical signals is in short presented. The reason for that is caused by the fact, that nowadays the broad analysis of various biomedical signals is extremely popular. The first method presented in this paper relies on kernel density estimators application. Implementation of such method enables construction of densitograms for the examined bio-signals. One of the biggest advantages of this method is that it allows to obtain statistically filtered signals, which results in making the whole signal processing task significantly quicker. The second method described in this paper is based on basic mathematical operations only. Despite its simplicity the whole process can be implemented on almost any hardware platform, including those with very limited computational capabilities. Also it makes the task quick. In accordance with the conducted experiments - the method is also efficient and as it can also be implemented on embedded platform and the algorithm can be rewritten in any programming language, the potential application of this method is wide.

[1]  G. Yue,et al.  Single-Trial EEG-EMG Coherence Analysis Reveals Muscle Fatigue-Related Progressive Alterations in Corticomuscular Coupling , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Huzefa Rangwala,et al.  Novel Method for Predicting Dexterous Individual Finger Movements by Imaging Muscle Activity Using a Wearable Ultrasonic System , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Mariusz Pelc,et al.  Application of Kernel Density Estimators for Analysis of EEG Signals , 2012, UCAmI.

[4]  Michael Kai Petersen,et al.  Smartphones as pocketable labs: visions for mobile brain imaging and neurofeedback. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[5]  Madeleine M. Lowery,et al.  Analysis of the effects of mechanically induced tremor on EEG-EMG coherence using wavelet and partial directed coherence , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[6]  S. Debener,et al.  How about taking a low-cost, small, and wireless EEG for a walk? , 2012, Psychophysiology.

[7]  Erik Scheme,et al.  Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Vijayan K. Asari,et al.  Electroencephelograph based brain machine interface for controlling a robotic arm , 2013, 2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[9]  Chenguang Yang,et al.  Controlling mobile Spykee robot using Emotiv Neuro headset , 2013, Proceedings of the 32nd Chinese Control Conference.

[10]  Zhanpeng Jin,et al.  A wearable real-time BCI system based on mobile cloud computing , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[11]  Ahmet Alkan,et al.  Identification of EMG signals using discriminant analysis and SVM classifier , 2012, Expert Syst. Appl..