International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 01 | Jan-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1192 MULTICLASS SUPPORT VECTOR MACHINE WITH NEW KERNEL FOR EEG CLASSIFICATION Mr.A.S.Muthanantha Murugavel,M.E.,M.B.A., Assistant Professor(SG) ,Department of Information Technology,Dr.Mahalingam college of Engineering and Technology,Pollachi,Tamilnadu,India. D. Akshaya, S. Anitha, M. Manjureka , T. Mohanapriya B.Tech Final year, Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The Electroencephalogram (EEG) is a complex and a periodic time series, which is a sum over a very large number of neuronal membrane potentials. In this Project we have proposed the Multiclass Support Vector Machines with new Kernel (MSVM) for EEG (Electroencephalogram) signals classification problem with hybrid domain features. The Feature Extraction and Classification are performed using the publicly available benchmark datasets. The wavelet transform (WT) is used to extract the time, frequency, wavelet coefficients of discrete time signals. The best among the features extracted are selected using fuzzy logic. Then we classify the selected features using the Multiclass SVM classification technique. Our new classification technique achieves higher classification accuracy and reduces the computational complexity than the existing techniques. Key Words: Electroencephalogram , Fuzzy logic , Wavelet Transform , Multiclass Support Vector Machine 1. INTRODUCTION The electrical nature of the human nervous system has been recognized for more than a century. It is well known that the variation of the surface potential distribution on the scalp reflects functional activities emerging from the underlying brain. This surface potential variation can be recorded by affixing an array of electrodes to the scalp, and measuring the voltage between pairs of these electrodes, which are then filtered, amplified, and recorded. The resulting data is called the Electroencephalogram (EEG). In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. In neurology, the main diagnostic application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. A secondary clinical use of EEG is in the diagnosis of coma and encephalopathy. EEG used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders, but this use has decreased with the advent of anatomical imaging techniques such as MRI and CT. Derivatives of the EEG technique include evoked potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, somato sensory, or auditory). Event-related potentials refer to averaged EEG responses that are time- locked to more complex processing of stimuli; this technique is used in cognitive science, cognitive psychology, and psycho physiological research. 1.1 NATURE OF EEG EEG reflects correlated synaptic activity caused by post- synaptic potentials of cortical neurons. The ionic currents involved in the generation of fast action potentials may not contribute greatly to the averaged field potentials representing the EEG. More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extra cellular ionic currents caused by dendritic electrical activity, whereas the fields producing magneto encephalographic signals are associated with intracellular ionic currents. The electric potentials generated by single neurons are far too small to be picked by EEG or MEG. EEG activity therefore always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation, radial to the scalp. Currents that are tangential to the scalp are not picked up by the EEG. The EEG therefore benefits from the parallel, radial arrangement of apical dendrites in the cortex. Because voltage fields fall off with the fourth power of the radius, activity from deep sources is more difficult to detect than currents near the skull.