Using Brain Computer Interfaces for Geometry Selection in CAD Systems: P300 Detection Approach Available to Purchase

The purpose of this paper is to explore the potential of brain-computer interfaces as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI for selecting different surface of geometrical objects in the CAD systems using the P300 wave. The P300 (P3) wave is an event related potential (ERP) elicited by infrequent, stimuli (target faces flashing). Users wear an electroencephalogram (EEG) headset and try to select a target face of an object. Different faces of the object randomly flash which make the flashing of target face, an infrequent event. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the discrete wavelet transforms (DWT) to detect the P300 component in the signal. The flashing face which causes the P300 component in the EEG signal is classified as the target face. Using a linear discriminant analysis, the target face is classified correctly with an average accuracy of 73.9%.