Title :
A comparison of techniques and technologies for SSVEP classification
Author :
Tello, Richard M. G. ; Muller, Sandra M. T. ; Bastos-Filho, Teodiano ; Ferreira, Andre
Author_Institution :
Post-Grad. Program of Electr. Eng. (PPGEE), UFES, Vitoria, Brazil
Abstract :
This paper presents the evaluation of seven techniques of feature extraction (PSD, F-Test, EMD, MCE, CCA, LASSO and MSI) for gaze-target detections in a SSVEP-based BCI. Two type of technologies for visual stimulation were used (LCD and LEDs). Five differents windows lengths (1, 2, 4, 5 and 10 s) were used and seven volunteers participated in this study. The highest accuracy obtained in all cases was 93.57% using LEDs and the highest ITR was 36.90 bits/min for LCD. The technique based on MSI shows the highest success rate in both cases (LCD or LED) and is even more noticeable when the window size is increased.
Keywords :
brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; synchronisation; visual evoked potentials; CCA; EEG; EMD; F-Test; ITR; LASSO; LCD; LEDs; MCE; MSI; PSD; SSVEP classification; SSVEP-based BCI; brain computer interface; canonical correlation analysis; feature extraction; gaze-target detections; information transfer rate; least absolute shrinkage-and-selection operator; multivariate synchronization index; steady-state visually evoked potentials; visual stimulation; Accuracy; Correlation; Electrodes; Electroencephalography; Feature extraction; Light emitting diodes; Visualization; Brain Computer Interface (BCI); EEG; Steady-State Visual Evoked Potentials (SSVEP);
Conference_Titel :
Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE
Conference_Location :
Salvador
Print_ISBN :
978-1-4799-5688-3
DOI :
10.1109/BRC.2014.6880956
