The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials

Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, the element that increases the ERPs amplitude is unclear, and how to test the effect of it on the brain is a problem. This study used different graphic structures of an NAO humanoid robot to design three types of robot stimuli: a global robot, its local information, and its topological action. Ten subjects first conducted an odd-ball-based BCI (OD-BCI) by applying these stimuli. Then, they accomplished a delayed matching-to-sample task (DMST) that was used to specialize the encoding and retrieval phases of the OD-BCI task. In the retrieval phase of the DMST, the global stimulus induces the largest N200 and P300 potentials with the shortest latencies in the frontal, central, and occipital areas. This finding is in accordance with the P300 and classification performance of the OD-BCI task. When induced by the local stimulus, the subjects responded faster and more accurately in the retrieval phase of the DMST than in the other two conditions, indicating that the local stimulus improved the subject’s responses. These results indicate that the OD-BCI task causes subject’s retrieval work when the subject recognizes and outputs the stimulus. The global stimulus that contains topological and local elements could make brain react faster and induce larger ERPs, this finding could be used during the development of visual stimuli to improve BCI performance.

[1]  Guang-Zhong Yang,et al.  A Self-Adaptive Online Brain–Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System , 2018, IEEE Transactions on Fuzzy Systems.

[2]  Effects of processing bias on the recognition of composite face halves , 2005, Psychonomic bulletin & review.

[3]  R T Knight,et al.  Differential Sources for 2 Neural Signatures of Target Detection: An Electrocorticography Study , 2016, Cerebral cortex.

[4]  Atsushi Senju,et al.  The two-process theory of biological motion processing , 2020, Neuroscience & Biobehavioral Reviews.

[5]  Martin B. Curry,et al.  Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity, and semantic distance for 239 symbols , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[6]  A Treisman,et al.  Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.

[7]  Tijl Grootswagers,et al.  Untangling featural and conceptual object representations , 2019, NeuroImage.

[8]  Sarah M. Kark,et al.  NEVER forget: negative emotional valence enhances recapitulation , 2017, Psychonomic Bulletin & Review.

[9]  Ivo Käthner,et al.  Rapid P300 brain-computer interface communication with a head-mounted display , 2015, Front. Neurosci..

[10]  Xingyu Wang,et al.  A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..

[11]  A. Kübler,et al.  Flashing characters with famous faces improves ERP-based brain–computer interface performance , 2011, Journal of neural engineering.

[12]  R. Doerge,et al.  Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping , 2017, G3: Genes, Genomes, Genetics.

[13]  Wei Li,et al.  Increasing N200 Potentials Via Visual Stimulus Depicting Humanoid Robot Behavior , 2016, Int. J. Neural Syst..

[14]  Yuanqing Li,et al.  An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential , 2010, IEEE Transactions on Biomedical Engineering.

[15]  Ramasubba Reddy M,et al.  Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Daniel Pérez-Marcos,et al.  Writing through a robot: a proof of concept for a brain-machine interface. , 2011, Medical engineering & physics.

[17]  Murat Akcakaya,et al.  Recursive Bayesian Coding for BCIs , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Andrzej Cichocki,et al.  The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  R. Buckner,et al.  The cognitive neuroscience og remembering , 2001, Nature Reviews Neuroscience.

[20]  Manuele Bicego,et al.  On the importance of local and global analysis in the judgment of similarity and dissimilarity of faces , 2019, Image Vis. Comput..

[21]  Ren Xu,et al.  Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm , 2020, IEEE Transactions on Biomedical Engineering.

[22]  M. Cella,et al.  Using ERPs to explore the impact of affective distraction on working memory stages in schizophrenia , 2018, Cognitive, affective & behavioral neuroscience.

[23]  L. Williams,et al.  Characterizing neurocognitive markers of familial risk for depression using multi-modal imaging, behavioral and self-report measures. , 2019, Journal of affective disorders.

[24]  Pei Sun,et al.  Consciousness modulates the automatic change detection of masked emotional faces: Evidence from visual mismatch negativity , 2020, Neuropsychologia.

[25]  Arkadiusz Kubacki,et al.  Controlling the industrial robot model with the hybrid BCI based on EOG and eye tracking , 2019, MATEC Web of Conferences.

[26]  Kazuki Yoshida,et al.  Focused attention meditation training modifies neural activity and attention: longitudinal EEG data in non-meditators , 2020, Social cognitive and affective neuroscience.

[27]  A. Cichocki,et al.  Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns , 2020, Frontiers in Neuroscience.

[28]  P. A. Helm A cognitive architecture account of the visual local advantage phenomenon in autism spectrum disorders , 2016, Vision Research.

[29]  Maria Lorna A. Kunnath,et al.  An experimental research study on the effect of pictorial icons on a user-learner's performance , 2007, Comput. Hum. Behav..

[30]  Guizhi Xu,et al.  The Study of Influence of Sound on Visual ERP-Based Brain Computer Interface , 2020, Sensors.

[31]  T. Demiralp,et al.  P3 response during short-term memory retrieval revisited by a spatio-temporal analysis. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[32]  M. Manosevitz High-Speed Scanning in Human Memory , .

[33]  Rami Saab,et al.  An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface , 2016, Int. J. Neural Syst..

[34]  Xiaogang Chen,et al.  Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm , 2019, Journal of neural engineering.

[35]  A. Cichocki,et al.  A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.

[36]  Martin Eimer,et al.  Electrophysiological Evidence for a Sensory Recruitment Model of Somatosensory Working Memory. , 2015, Cerebral cortex.

[37]  Yang-Kun Ou,et al.  Effects of sign design features and training on comprehension of traffic signs in Taiwanese and Vietnamese user groups , 2012 .

[38]  Jintao Zhang,et al.  An Online Interactive Paradigm for P300 Brain–Computer Interface Speller , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[39]  Yi-Li Tseng,et al.  Lingering Sound: Event-Related Phase-Amplitude Coupling and Phase-Locking in Fronto-Temporo-Parietal Functional Networks During Memory Retrieval of Music Melodies , 2019, Front. Hum. Neurosci..

[40]  K. Hiraki,et al.  An event-related potentials study of biological motion perception in human infants. , 2005, Brain research. Cognitive brain research.

[41]  M. Koivisto,et al.  Neuronavigated TMS of early visual cortex eliminates unconscious processing of chromatic stimuli , 2019, Neuropsychologia.

[42]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

[43]  Uwe Herwig,et al.  Using the International 10-20 EEG System for Positioning of Transcranial Magnetic Stimulation , 2004, Brain Topography.

[44]  Zhi-Hua Zhou,et al.  Making FLDA applicable to face recognition with one sample per person , 2004, Pattern Recognit..

[45]  L. Phillips,et al.  Visual attention, biological motion perception, and healthy ageing , 2018, Psychological Research.

[46]  Yezhong Tang,et al.  Sex differences in vocalization are reflected by event-related potential components in the music frog , 2020, Animal Cognition.

[47]  G Townsend,et al.  Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain. , 2016, Journal of neural engineering.

[48]  Andrzej Cichocki,et al.  Correlation-based channel selection and regularized feature optimization for MI-based BCI , 2019, Neural Networks.

[49]  Shaozi Li,et al.  Cover patches: A general feature extraction strategy for spoofing detection , 2019, Concurr. Comput. Pract. Exp..

[50]  J. Ragland,et al.  Recollection and Familiarity in Schizophrenia: A Quantitative Review , 2013, Biological Psychiatry.

[51]  Tim Gollisch,et al.  Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina , 2010, Neuron.

[52]  Zuo Juan ERP study on the human error in delayed matching-to-sample task paradigm , 2013 .

[53]  Qi Li,et al.  Happy emotion cognition of bimodal audiovisual stimuli optimizes the performance of the P300 speller , 2019, Brain and behavior.

[54]  Benjamin Wittevrongel,et al.  N‐back training and transfer effects revealed by behavioral responses and EEG , 2018, Brain and behavior.

[55]  Mohammad Bagher Shamsollahi,et al.  A novel dual and triple shifted RSVP paradigm for P300 speller , 2019, Journal of Neuroscience Methods.

[56]  Yuanqing Li,et al.  A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control , 2013, IEEE Transactions on Biomedical Engineering.

[57]  Yue-jia Luo,et al.  Stage effects of negative emotion on spatial and verbal working memory , 2010, BMC Neuroscience.

[58]  Rifai Chai,et al.  Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System , 2017, IEEE Journal of Biomedical and Health Informatics.

[59]  Fumitoshi Matsuno,et al.  A Novel EOG/EEG Hybrid Human–Machine Interface Adopting Eye Movements and ERPs: Application to Robot Control , 2015, IEEE Transactions on Biomedical Engineering.

[60]  M. Lévesque Perception , 1986, The Yale Journal of Biology and Medicine.

[61]  Vera Maljkovic,et al.  Two types of image generation: Evidence for left and right hemisphere processes , 1995, Neuropsychologia.

[62]  Feng Duan,et al.  A Human-Vehicle Collaborative Simulated Driving System Based on Hybrid Brain–Computer Interfaces and Computer Vision , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[63]  Adam Gazzaley,et al.  Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. , 2008, Cerebral cortex.

[64]  Reza Shoorangiz,et al.  Predicting Microsleep States Using EEG Inter-Channel Relationships , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[65]  Michael J Kahana,et al.  Compound cuing in free recall. , 2014, Journal of experimental psychology. Learning, memory, and cognition.

[66]  Helge Ritter,et al.  Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[67]  Jie Li,et al.  Evaluation and Application of a Hybrid Brain Computer Interface for Real Wheelchair Parallel Control with Multi-Degree of Freedom , 2014, Int. J. Neural Syst..

[68]  M. Farah,et al.  What is "special" about face perception? , 1998, Psychological review.

[69]  Christine E. Weber,et al.  Differentiation of subsequent memory effects between retrieval practice and elaborative study , 2017, Biological Psychology.

[70]  Lei Ding,et al.  Combining multiple features for error detection and its application in brain–computer interface , 2016, Biomedical engineering online.

[71]  Yufeng Ke,et al.  3D Stimulus Presentation of ERP-Speller in Virtual Reality* , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).

[72]  Andreas Hein,et al.  Counteracting the Slowdown of Reaction Times in a Vigilance Experiment With 40-Hz Transcranial Alternating Current Stimulation , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[73]  M. Kuba,et al.  Visual evoked potentials specific for motion onset , 2004, Documenta Ophthalmologica.

[74]  Stefano Passini,et al.  Icon-function relationship in toolbar icons , 2008, Displays.

[75]  Greeshma Sharma,et al.  Effect of Complexity on Frontal Event Related Desynchronisation in Mental Rotation Task , 2019, Applied Psychophysiology and Biofeedback.

[76]  Feng Duan,et al.  A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals , 2019, IEEE Access.

[77]  Chun-yan Guo,et al.  Examining the neural mechanism behind testing effect with concrete and abstract words , 2019, Neuroreport.

[78]  M. Lappe,et al.  Visual areas involved in the perception of human movement from dynamic form analysis , 2005, Neuroreport.

[79]  Feng Duan,et al.  An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles , 2017, IEEE Transactions on Industrial Electronics.

[80]  Elizabeth A. Kensinger,et al.  Prior Emotional Context Modulates Early Event-Related Potentials to Neutral Retrieval Cues , 2019, Journal of Cognitive Neuroscience.

[81]  Qian Cai,et al.  The effect of a virtual reality learning environment on learners’ spatial ability , 2018, Virtual Reality.