A P300-Based BCI Classification Algorithm Using Least Square Support Vector Machine

نویسندگان

  • Vanitha Narayan Raju
  • In-Ho Ra
  • Ravi Sankar
چکیده

In this paper, we propose a classification algorithm for P300-based Brain Computer Interface (BCI). Brain Computer Interface (BCI) with P300 speller helps Amyotrophic Lateral Sclerosis (ALS) patients to spell words with the help of their brain signal activities. Amyotrophic Lateral Sclerosis (ALS) also known as Lou Gehrig's disease in which certain nerve cells in brain and spinal cord also called as motor neurons, slowly die. The main goal of the proposed research is to develop classification algorithms for P300-based Brian Computer Interface (BCI). The proposed model can be used to restore basic communicating ability for Amyotrophic Lateral Sclerosis (ALS) patients in a reliable and fast way.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

P65: Speech Recognition Based on Bbrain Signals by the Quantum Support Vector Machine for Inflammatory Patient ALS

People communicate with each other by exchanging verbal and visual expressions. However, paralyzed patients with various neurological diseases such as amyotrophic lateral sclerosis and cerebral ischemia have difficulties in daily communications because they cannot control their body voluntarily. In this context, brain-computer interface (BCI) has been studied as a tool of communication for thes...

متن کامل

Feature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine

We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...

متن کامل

Application of Genetic Algorithm Based Support Vector Machine Model in Second Virial Coefficient Prediction of Pure Compounds

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...

متن کامل

Self-training Algorithm for Channel Selection in P300-Based BCI Speller

In this paper, we address the important problem of channel selection for a P300-based brain computer interface (BCI) speller system in the situation of insufficient training data with labels. An iterative semi-supervised support vector machine (SVM) is proposed for time segment selection as well as classification, in which both labeled training data and unlabeled test data are utilized. The per...

متن کامل

A COMPARATIVE ANALYSIS OF WAVELET-BASED FEMG SIGNAL DENOISING WITH THRESHOLD FUNCTIONS AND FACIAL EXPRESSION CLASSIFICATION USING SVM AND LSSVM

This work presents a technique for the analysis of Facial Electromyogram signal activities to classify five different facial expressions for Computer-Muscle Interfacing applications. Facial Electromyogram (FEMG) is a technique for recording the asynchronous activation of neuronal inside the face muscles with non-invasive electrodes. FEMG pattern recognition is a difficult task for the researche...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015