Comparative Analysis of Cognitive Neurodynamics on AMIGOS Dataset Versus Prepared Dataset

Cognitive Neurodynamics is the scientific field that is concerned with the study of biological processes of brain and aspects that underlie cognition. The specific focus of cognition is on neural connections that are involved in the mental process. So the resultant of cognitive states which consists of thoughts, perception, memory, experiences predicted the state of emotional behaviour in human. There are two parts of brain which are responsible for cognition and emotional states in human i.e. Amygdala and frontal cortex of brain. In this paper, a correlation analysis is being done on the basis of common feature set choosen between self- prepared dataset and public access dataset. The public domain dataset named AMIGOS is choosen for research analysis, as it is prepared on (14 + 2) electrodes. In both datasets same number of electrodes are used. Experimental results confirm that accuracy of both datasets are compatible with each other. AMIGOS dataset shows 80.12% accuracy and prepared dataset shows 74.62% accuracy using SVM classifier.

[1]  J. Russell.  A circumplex model of affect. , 1980 .

[2]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[3]  Norman G. Poythress,et al.  Training in Law and Psychology: Models from the Villanova Conference , 1997 .

[4]  Robert Plutchik,et al.  The circumplex as a general model of the structure of emotions and personality. , 1997 .

[5]  D. J. Jenkins.  Foods that Harm, Foods that Heal: An A-Z Guide to Safe and Healthy Eating , 1997 .

[6]  Gregory P. Lee,et al.  Different Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-Making , 1999, The Journal of Neuroscience.

[7]  H. Damasio,et al.  Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. , 2000, Brain : a journal of neurology.

[8]  E. Rolls,et al.  Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. , 2005, Journal of neurophysiology.

[9]  Hongbin Wang,et al.  Cognitive-Affective Interactions in Human decision-Making: A Neurocomputational Approach , 2006 .

[10]  Harald Atmanspacher,et al.  Interpreting neurodynamics: concepts and facts , 2008, Cognitive Neurodynamics.

[11]  Laura Astolfi,et al.  The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology , 2009, Comput. Intell. Neurosci..

[12]  D. Consoli.  A New Concept of Marketing: The Emotional Marketing , 2010 .

[13]  E. Fehr,et al.  Dorsolateral and ventromedial prefrontal cortex orchestrate normative choice , 2011, Nature Neuroscience.

[14]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.

[15]  Joseph W. Kable,et al.  The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value , 2013, NeuroImage.

[16]  Jinde Cao,et al.  Neurodynamic System Theory and Applications , 2013 .

[17]  Sabri Arik,et al.  An Analysis of Stability of a Class of Neutral-Type Neural Networks with Discrete Time Delays , 2013 .

[18]  Nikola K. Kasabov,et al.  NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data , 2014, Neural Networks.

[19]  S. S. Lekshmi,et al.  EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network , 2014, 2014 International Conference on Communication and Signal Processing.

[20]  Lorena R. R. Gianotti,et al.  Diminishing parochialism in intergroup conflict by disrupting the right temporo-parietal junction. , 2014, Social cognitive and affective neuroscience.

[21]  Fabio Babiloni,et al.  How to Measure Cerebral Correlates of Emotions in Marketing Relevant Tasks , 2014, Cognitive Computation.

[22]  X. Bornas,et al.  Spontaneous EEG activity and spontaneous emotion regulation. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[23]  Ale Smidts,et al.  Brain Responses to Movie Trailers Predict Individual Preferences for Movies and Their Population-Wide Commercial Success , 2015 .

[24]  Asha Rani,et al.  Classification of human emotions from EEG signals using SVM and LDA Classifiers , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[25]  Hyo Jong Lee,et al.  Deep learninig of EEG signals for emotion recognition , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[26]  Rytis Maskeliunas,et al.  Distributed under Creative Commons Cc-by 4.0 Consumer-grade Eeg Devices: Are They Usable for Control Tasks? , 2022 .

[27]  G. Jayachandran Nair,et al.  Extracting the features of emotion from EEG signals and classify using affective computing , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[28]  Nick Lee,et al.  This is your brain on neuromarketing: reflections on a decade of research , 2017 .

[29]  Debi Prosad Dogra,et al.  Analysis of EEG signals and its application to neuromarketing , 2017, Multimedia Tools and Applications.

[30]  E. Ruiz-Padial,et al.  Fractal dimension of EEG signals and heart dynamics in discrete emotional states , 2018, Biological Psychology.

[31]  Joseph Ciorciari,et al.  Consumer neuroscience for marketing researchers , 2018 .

[32]  Reginald B. Adams,et al.  Neurodynamics and connectivity during facial fear perception: The role of threat exposure and signal congruity , 2017, Scientific Reports.

[33]  Stephen José Hanson,et al.  Attentional Bias in Human Category Learning: The Case of Deep Learning , 2018, Front. Psychol..

[34]  Stefan Winkler,et al.  ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors , 2018, IEEE Transactions on Affective Computing.

[35]  Terry Daugherty,et al.  Measuring consumer neural activation to differentiate cognitive processing of advertising , 2018 .

[36]  Jaiteg Singh,et al.  Cognitive Emotion Measures of Brain , 2019, 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom).

[37]  Rollin McCraty,et al.  Heart-Brain Neurodynamics , 2014, Advances in Psychology, Mental Health, and Behavioral Studies.