Publication | Closed Access
A comparison of techniques and technologies for SSVEP classification
41
Citations
20
References
2014
Year
Unknown Venue
EngineeringMachine LearningBiometricsDiagnosisFeature ExtractionSsvep ClassificationAttentionSupport Vector MachineClassification MethodData ScienceData MiningPattern RecognitionWindow SizeOphthalmologyKnowledge DiscoveryVision ResearchComputer ScienceBrain-computer InterfaceVisual FunctionSsvep-based BciEeg Signal ProcessingEye TrackingClassifier SystemBraincomputer InterfaceMedicine
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.
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