Fragmentary shape recognition: A BCI study

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. 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.

[1]  B. Falcidieno,et al.  Invited Lecture: A Shape Abstraction Paradigm for Modeling Geometry and Semantics , 1998 .

[2]  Cláudio T. Silva,et al.  A User Study of Visualization Effectiveness Using EEG and Cognitive Load , 2011, Comput. Graph. Forum.

[3]  Ehsan T. Esfahani,et al.  Using Brain Computer Interfaces for Geometry Selection in CAD Systems: P300 Detection Approach , 2011 .

[4]  Ramon Parra-Loera.  Partial shape recognition of 2-D shapes using normalized interval vertex descriptors , 1992, Other Conferences.

[5]  Marc Alexa,et al.  A descriptor for large scale image retrieval based on sketched feature lines , 2009, SBIM '09.

[6]  Ricardo Gutierrez-Osuna,et al.  GestureCommander: continuous touch-based gesture prediction , 2012, CHI Extended Abstracts.

[7]  Ehsan T. Esfahani,et al.  EVALUATION OF SSVEP AS PASSIVE FEEDBACK FOR IMPROVING THE PERFORMANCE OF BRAIN MACHINE INTERFACES , 2012 .

[8]  I. Biederman,et al.  Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. , 1993 .

[9]  Michael Oltmans.  Envisioning sketch recognition: a local feature based approach to recognizing informal sketches , 2007 .

[10]  Sven J. Dickinson,et al.  Panel report: the potential of geons for generic 3-D object recognition , 1997, Image Vis. Comput..

[11]  Niloy J. Mitra,et al.  Abstraction of man-made shapes , 2009, SIGGRAPH 2009.

[12]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[13]  Irving Biederman,et al.  One-shot viewpoint invariance in matching novel objects , 1999, Vision Research.

[14]  Martin D. Levine,et al.  3-D shape approximation using parametric geons , 1997, Image Vis. Comput..

[15]  Ehsan Tarkesh Esfahani,et al.  Investigation of Brain Computer Interface as a New Modality in Computer Aided Design/Engineering Systems , 2012 .

[16]  Mark Meyer,et al.  Implicit fairing of irregular meshes using diffusion and curvature flow , 1999, SIGGRAPH.

[17]  Silvia Biasotti,et al.  Shape Abstraction Using Computational Topology Techniques , 2002, Workshop on Geometric Modeling.

[18]  Marc Alexa,et al.  Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors , 2011, IEEE Transactions on Visualization and Computer Graphics.

[19]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[20]  J. Koenderink,et al.  The internal representation of solid shape with respect to vision , 1979, Biological Cybernetics.

[21]  Rahul Rai,et al.  BCI-Touch Based System: A Multimodal CAD Interface for Object Manipulation , 2013 .

[22]  Levent Burak Kara,et al.  Co-abstraction of shape collections , 2012, ACM Trans. Graph..

[23]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[24]  F. Paas,et al.  Cognitive Load Theory and Instructional Design: Recent Developments , 2003 .

[25]  Michela Spagnuolo,et al.  A shape abstraction paradigm for modelling geometry and semantics , 1998, Proceedings. Computer Graphics International (Cat. No.98EX149).

[26]  I. Biederman,et al.  Neural evidence for intermediate representations in object recognition , 2006, Vision Research.

[27]  Marc Alexa,et al.  Sketch-based shape retrieval , 2012, ACM Trans. Graph..

[28]  Lee R. Nackman,et al.  Three-Dimensional Shape Description Using the Symmetric Axis Transform I: Theory , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[30]  Eric Cooper,et al.  Geon Theory as an Account of Shape Recognition in Mind, Brain and Machine , 1993, BMVC.

[31]  Joydeep Bhattacharya,et al.  Correction of blink artifacts using independent component analysis and empirical mode decomposition. , 2010, Psychophysiology.

[32]  Leonidas J. Guibas,et al.  Discovering structural regularity in 3D geometry , 2008, SIGGRAPH 2008.

[33]  A. Baddeley.  Working Memory: The Interface between Memory and Cognition , 1992, Journal of Cognitive Neuroscience.

[34]  A. Murat Tekalp,et al.  Partial shape recognition by sub-matrix matching for partial matching guided image labeling , 2005, Pattern Recognit..

[35]  Hugues Hoppe,et al.  Progressive meshes , 1996, SIGGRAPH.

[36]  Martin D. Levine,et al.  Parametric geons: a discrete set of shapes with parameterized attributes , 1994, Defense, Security, and Sensing.

[37]  Richard E. Mayer,et al.  Cognitive Theory of Multimedia Learning , 2021, The Cambridge Handbook of Multimedia Learning.

[38]  Rahul Rai,et al.  Human factors study on the usage of BCI headset for 3D CAD modeling , 2014, Comput. Aided Des..

[39]  Richard E. Mayer,et al.  The Cambridge Handbook of Multimedia Learning: Cognitive Theory of Multimedia Learning , 2005 .

[40]  Marco Attene,et al.  Hierarchical mesh segmentation based on fitting primitives , 2006, The Visual Computer.

[41]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[42]  Gary Brown,et al.  A Non-Linear Shape Abstraction Technique , 1993, CAIP.

[43]  Xiaofeng Mi,et al.  Abstraction of 2D shapes in terms of parts , 2009, NPAR '09.