A new approach to detect P300 in a single trial based on PCA and SVM classifier

Autor:	P. M. Shyam Prasad, N. C. Shivprakash, Avinash G. Keskar, Radhika Swarnkar
Rok vydání:	2016
Předmět:	Discrete wavelet transform Support vector machine Wavelet Structured support vector machine business.industry Computer science Stationary wavelet transform Margin classifier Principal component analysis Pattern recognition Artificial intelligence business Classifier (UML)
Zdroj:	2016 IEEE Region 10 Symposium (TENSYMP).
DOI:	10.1109/tenconspring.2016.7519432
Popis:	Single trial detection of P300 signal is one of the trending areas of Brain Computer Interface (BCI) research. We propose a new method with a high level of accuracy to detect P300 signals in a single trial. Features were obtained with a new technique making use of the wavelet coefficients. Reduced feature dimension was achieved using Principal Component Analysis (PCA). Support Vector Machine (SVM) was used as the classifier. The proposed method has achieved an accuracy of 98.47% for Subject A and 95.06% for Subject B. Thus a high degree of accuracy was obtained.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fb05df2362a6f864fb07d2e67dbc7be5 https://doi.org/10.1109/tenconspring.2016.7519432 Zobrazit plný text záznamu