Fragmentary shape recognition: A BCI study. (February 2016)

• Record Type: Journal Article
• Authors: Rai, Rahul; Deshpande, Akshay V.
• Is Part Of: Computer aided design. Volume 71(2016)
• Page Start: 51
• Page End: 64
• Publication Date: 2016-02
• Subjects: Brain–Computer Interfaces (BCI) -- Fragmentary shape recognition -- User studies -- Cognitive load in shape processing -- Natural interactions; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2015.10.001
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Recently, Brain–Computer Interface (BCI) has emerged as a potential modality that utilizes natural and intuitive human mechanisms of thinking process to enable interactions in CAD interfaces. Before BCI could become a mainstream mode of HCI for CAD interfaces; fundamental studies directed towards understanding how humans mentally represent and process the geometry are needed. The outlined work in this paper presents an objective user study to understand shape recognition process in the humans. Specifically, we focus on the fundamental task of fragmentary shape identification. The problem of fragmentary shape recognition can be defined as follows: given a partial and incomplete minimalistic representation of a given shape, can one recognize the actual complete shape or object? In user studies, each subject was progressively (in stages) shown more informative fragmented images of an object to be recognized. During each stage of the experiment, the brain activity of users in the form of electroencephalogram (EEG) signals was recorded with a BCI headset. The recorded signals are then processed to objectively study the fragmentary shape recognition process. The results of user studies conclusively show that the measured brain activities of subjects can serve as a very accurate proxy to estimate subjects fragmentary shape recognition process. Highlights: BCI based fragmentary shape recognition process. Innovative use of BCI in shape recognition domain. Power spectral density and cognitive load estimation. IRB approved study to assess the fragmentary shape recognition process.