NDLI: Comparison between Effective Features Used for the Bayesian and the SVM Classifiers in BCI

Content Provider	IEEE Xplore Digital Library
Author	Arbabi, E. Shamsollahi, M.B. Sameni, R.
Copyright Year	2005
Abstract	Brain-computer interface (BCI) is based on processing signals recorded from the scalp, the surface of the cortex or from the inside of the brain in order to identify desired actions or behaviors. In BCI we are interested in extracting the most effective features from rare data in order to have the desired classification results. In this paper besides proposing two discrimination algorithms for classifying imagined movements of the left small finger and the tongue, a comparison has been done between the effective features applied by the Bayesian and the SVM classifiers for the BCI task. In fact the comparison was done on the most effective features found from a pool of extracted features for each classifier, separately. Finally using the most effective features of each classifier, the classification accuracy of 89.21% and 91.01% were achieved for the Bayesian and the SVM classifiers, respectively
Starting Page	5365
Ending Page	5368
File Size	225968
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615694
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Support vector machines Support vector machine classification Brain computer interfaces Signal processing Scalp Data mining Fingers Tongue Feature extraction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Machine Learning Methodologies in Brain-Computer Interface Systems

Distinguishing Between Left and Right Finger Movement from EEG using SVM

A New Classification Method Based on KF-SVM in Brain Computer Interfaces

Signal processing for brain-computer interface: enhance feature extraction and classification

Determining AR order for BCI based on motor imagery

Detection of Facial Components Based on SVM Classification and Invariant Feature

Feature Extraction and Parameters Selection of Classification Model on Brain-Computer Interface

Training SVMs for Multiple Features Classification Problems

Classification of eeg data using fht and svm based on bayesian network.

Comparison between Effective Features Used for the Bayesian and the SVM Classifiers in BCI

Similar Documents

Machine Learning Methodologies in Brain-Computer Interface Systems

Distinguishing Between Left and Right Finger Movement from EEG using SVM

A New Classification Method Based on KF-SVM in Brain Computer Interfaces

Signal processing for brain-computer interface: enhance feature extraction and classification

Determining AR order for BCI based on motor imagery

Detection of Facial Components Based on SVM Classification and Invariant Feature

Feature Extraction and Parameters Selection of Classification Model on Brain-Computer Interface

Training SVMs for Multiple Features Classification Problems

Classification of eeg data using fht and svm based on bayesian network.

Comparison between Effective Features Used for the Bayesian and the SVM Classifiers in BCI