Classification of EEG Signals Based on Image Representation of Statistical Features

This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxation, concentration, or neutral states. Using publicly available EEG data from a Muse Electroencephalography headband, a large number of features describing the wave are extracted, and subsequently reduced to 256 based on the Information Gain measure. These 256 features are then normalised and reshaped into a \(16\times 16\) grid, which can be expressed as a grayscale image. A deep Convolutional Neural Network is then trained on this data in order to classify the mental state of subjects. The proposed method obtained an out-of-sample classification accuracy of 89.38%, which is competitive with the 87.16% of the current best method from a previous work.

[1]  Anikó Ekárt,et al.  A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction , 2019, Complex..

[2]  Valery Naranjo,et al.  EvoDeep: A new evolutionary approach for automatic Deep Neural Networks parametrisation , 2018, J. Parallel Distributed Comput..

[3]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[4]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[5]  Tom Leonard,et al.  The Matrix-Logarithmic Covariance Model , 1996 .

[6]  Daniel Zwillinger,et al.  CRC Standard Probability and Statistics Tables and Formulae, Student Edition , 1999 .

[7]  Alan V. Oppenheim,et al.  Signals & systems (2nd ed.) , 1996 .

[8]  L. Rothkrantz,et al.  Toward an affect-sensitive multimodal human-computer interaction , 2003, Proc. IEEE.

[9]  Dongrui Wu,et al.  On the Vulnerability of CNN Classifiers in EEG-Based BCIs , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Daphne N. Yu,et al.  High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. , 1997, Cerebral cortex.

[11]  Rosalind W. Picard.  Affective Computing , 1997 .

[12]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[13]  Fillia Makedon,et al.  Brain-EE: Brain Enjoyment Evaluation using Commercial EEG Headband , 2016, PETRA.

[14]  R. Caton.  The Electric Currents of the Brain , 1970 .

[15]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Lauren C. Frey,et al.  Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants , 2016 .

[17]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[18]  Rodolfo R. Llinás,et al.  Intrinsic electrical properties of mammalian neurons and CNS function: a historical perspective , 2014, Front. Cell. Neurosci..

[19]  Anikó Ekárt,et al.  Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology , 2019 .

[20]  Dinh Van Huynh,et al.  Algebra and Its Applications , 2006 .

[21]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Michele Magno,et al.  Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor , 2018 .

[23]  Raymond Chiong,et al.  Deep Learning for Human Affect Recognition: Insights and New Developments , 2019, IEEE Transactions on Affective Computing.

[24]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[25]  Nuno Lourenço,et al.  DENSER: deep evolutionary network structured representation , 2018, Genetic Programming and Evolvable Machines.

[26]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[27]  C. Loan.  Computational Frameworks for the Fast Fourier Transform , 1992 .

[28]  Bharti W. Gawali,et al.  Correlation of EEG Images and Speech Signals for Emotion Analysis , 2015 .

[29]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[30]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[31]  Samit Bhattacharya,et al.  Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.

[32]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[33]  Leslie Pérez Cáceres,et al.  The irace package: Iterated racing for automatic algorithm configuration , 2016 .

[34]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[36]  Anikó Ekárt,et al.  A Study on Mental State Classification using EEG-based Brain-Machine Interface , 2018, 2018 International Conference on Intelligent Systems (IS).

[37]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Michele Magno,et al.  Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array , 2018 .