Authors:
Felipe Alberto Capati
;
Rodrigo Prior Bechelli
and
Maria Claudia F. Castro
Affiliation:
Centro Universitário da FEI, Brazil
Keyword(s):
BCI, SSVEP, P300, Evoked Potential, Visual Stimulus, LDA, SVM, FFT.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cybernetics and User Interface Technologies
;
Data Manipulation
;
Detection and Identification
;
Devices
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Information and Systems Security
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This paper presents a two stage Brain Computer Interface (BCI) keyboard system that consumes Electroencephalography (EEG) signals based on two evoked potential detection methods: P300 and Steady-State Visual Evoked Potential (SSVEP). In order to develop a practical daily use EEG system, signals were captured with a standard low cost Emotiv-EPOC system and processed using OpenViBE platform. Fast Fourier Transform (FFT) and sample average were used as feature extraction methods while Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were used as classifiers.