Go to:
Logótipo
Comuta visibilidade da coluna esquerda
Você está em: Start > Publications > View > Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair
Publication

Publications

Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair

Title
Cerebral Palsy EEG signals Classification: Facial Expressions and Thoughts for Driving an Intelligent Wheelchair
Type
Article in International Conference Proceedings Book
Year
2012
Authors
faria, bm
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
lau, n
(Author)
Other
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page Without ORCID
Conference proceedings International
Pages: 33-40
12th IEEE International Conference on Data Mining (ICDM)
Brussels, BELGIUM, DEC 10-13, 2012
Scientific classification
FOS: Natural sciences > Computer and information sciences
Other information
Authenticus ID: P-005-KEV
Abstract (EN): Brain Computer Interfaces (BCI) enables interaction between users and hardware systems, through the recognition of brainwave activity. However, the current BCI systems still have a very low accuracy on the recognition of facial expressions and thoughts. This makes it very difficult to use these devices to enable safe and robust commands of complex devices such as an Intelligent Wheelchair. This paper presents an approach to expand the use of a brain computer interface for driving an intelligent wheelchair by patients suffering from cerebral palsy. The approach was based on appropriate signal preprocessing based on Hjorth parameters, a forward approach for variable selection and several data mining algorithms for classification such as naive Bayes, neural networks and support vector machines. Experiments were performed using 30 individuals suffering from IV and V degrees of cerebral palsy on the Gross Motor Function (GMF) measure. The results achieved showed that the preprocessing and variable selection methods were effective enabling to improve the results of a commercial BCI product by 57%. With the developed system it was also possible for users to perform a circuit in a simulated environment using just facial expressions and thoughts.
Language: English
Type (Professor's evaluation): Scientific
Contact: btf@estsp.ipp.pt; lpreis@dsi.uminho.pt; nunolau@ua.pt
No. of pages: 8
Documents
We could not find any documents associated to the publication.
Related Publications

Of the same authors

A Methodology for Creating an Adapted Command Language for Driving an Intelligent Wheelchair (2015)
Article in International Scientific Journal
Faria, BM; reis, lp; lau, n
Patient classification and automatic configuration of an intelligent wheelchair (2013)
Article in International Conference Proceedings Book
faria, bm; reis, lp; lau, n; soares, jc; vasconcelos, s
Manual, Automatic and Shared Methods for Controlling an Intelligent Wheelchair Adaptation to Cerebral Palsy Users (2013)
Article in International Conference Proceedings Book
faria, bm; reis, lp; lau, n
Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams (2010)
Article in International Conference Proceedings Book
faria, bm; reis, lp; lau, n; castillo, g

See all (9)

Recommend this page Top
Copyright 1996-2025 © Faculdade de Direito da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z
Page created on: 2025-07-18 at 16:40:27 | Privacy Policy | Personal Data Protection Policy | Whistleblowing