Top

Evolutionary Intelligence

Published in:

08-03-2021 | Research Paper

Classify four imagined objects with EEG signals

Authors: Fabio R. Llorella Costa, Eduardo Iáñez, José M. Azorín, Gustavo Patow

Published in: Evolutionary Intelligence | Issue 3/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

EEG signals contain information directly related to cognitive activity. This paper presents a method to classify the images a person imagines via the information provided by the EEG signals. The images relating to the objects ‘tree’, ‘house’, ‘plane’ and ‘dog’ have been reconstructed. We have used a convolutional neural networks to obtain the reconstruction of the images and a genetic algorithm to find the parameters of this network. The results obtained have been evaluated by means of a Chebychev metric to compare the images, and it shows that the reconstruction is performed with a success of 57% over chance, with an accuracy in the classification of 60% and a kappa value of 0.40, demonstrating that the classification of five mental states where four of them come from the visual imagery, is possible.

previous article Balancing broad and deep searches in evolutionary computation via a parallel zoning search

next article A modified unconscious search algorithm for data clustering

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Costa A, Hortal E, Iánez E, Azorín JM (2014) A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm. PLoS One. https://doi.org/10.1371/journal.pone.0112352CrossRef

Costa A, Beda A, Iánez E, Azorín JM (2014) Control of a 2 DoF robot using a brain-machine interface. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2014.02.018CrossRef

Cincotti F, Mattia D, Aloise F, Bufalari S, Schalk G, Oriolo G et al (2008) Non-invasive brain-computer interface system: Towards its application as assistive technology. Brain Res Bull. https://doi.org/10.1016/j.brainresbull.2008.01.007CrossRef

Mak JN, Wolpaw JR (2009) Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev Biomed Eng. https://doi.org/10.1109/RBME.2009.2035356CrossRef

Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R (2005) EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci Lett. https://doi.org/10.1016/j.neulet.2005.03.021CrossRef

Lecuyer A, Lotte F, Reilly R (2008) Brain-computer interfaces, virtual reality and videogames IEEE computer. IEEE Comput. https://doi.org/10.1109/MC.2008.410CrossRef

Gunther FH, Brumberg JS, Wright EJ, Nieto-Catanon A, Tourville JA (2009) A wireless brain-machine interface for real-time speech synthesis. PLoS One. https://doi.org/10.1371/journal.pone.0008218CrossRef

Keih SC, Gottschalt L, Teichlein E, Weilbach FX (2016) Toward a P300 based brain-computer interface for aphasia rehabilitation after Stroke: lot feasibility study. Front Human Neurosci. https://doi.org/10.3389/fnhum.2016.00547CrossRef

Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M (1997) EEG based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. https://doi.org/10.1016/S0013-4694(97)00080-1CrossRef

10.

Wolpaw JR, McFarland DJ (1991) An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol. https://doi.org/10.1016/0013-4694(91)90040-BCrossRef

11.

Trejo LJ, Rosipal R, Matthews B (2006) Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Eng Med Biol Soci. https://doi.org/10.1109/TNSRE.2006.875578CrossRef

12.

Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng 10(1109/86):847808. https://doi.org/10.1109/86.847808

13.

Fabiani M, Gratton G, Karis D, Donchin E (1987) Definition, identification, and reliability of the P300 component of the event-related brain potential. Adv Psychophysiol 2:1–78

14.

Nijboer F, Sellers EW, Mellinger J, Jordan AM, Matuz T, Furdea A et al (2008) A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2008.03.034CrossRef

15.

Ortner R, Allison BZ, Korisek G, Gaggl H, Pfurtscheller G (2011) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng. https://doi.org/10.1109/TNSRE.2010.2076364CrossRef

16.

Kosmyna N, Lindgren JT, Lécuyer A (2018) Attending to visual stimuli versus performing visual imagery as a control strategy for EEG-based brain-computer interfaces. Sci Rep. https://doi.org/10.1038/s41598-018-31472-9CrossRef

17.

Nemrodov D, Niemeier M, Patel A, Nestor A (2018) The neural dynamics of facial identity processing: insights from EEG-based pattern analysis and image reconstruction. ENeuro. https://doi.org/10.1523/ENEURO.0358-17.2018CrossRef

18.

Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, Zhavoronkov A (2011) Brain-computer interface based on generation of visual images. PLoS One. https://doi.org/10.1371/journal.pone.0020674CrossRef

19.

Esfahani ET, Sundararajan V (2012) Classification of primitive shapes using brain computer interfaces. Comput Aided Des. https://doi.org/10.1016/j.cad.2011.04.008CrossRef

20.

Knauff M, Kassubek J, Mulack T, Greenle MW (2000) Cortical activation evoked by visual mental imagery as measured by functional MRI. Neuroreport. https://doi.org/10.1097/00001756-200012180-00011CrossRef

21.

Guohua S, Horikawa T, Majima K, Kamitani Y (2019) Deep image reconstruction from human brain activity. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1006633CrossRef

22.

Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRef

23.

Chollet F et al (2015) Keras. GitHub. Retrieved from http://github.com/fchollet/keras

24.

Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V et al (2010) OpenViBE: An open-source software platform to design, test and use brain-computer interfaces in real and virtual environments. Presence Teleoperators Virtual Environ. https://doi.org/10.1162/pres.19.1.35CrossRef

25.

Syahrull H, Lakany H, Ahmad RB, Bernard A, Conway (2017) Comparing common average referencing to laplacian referencing in detecing imagination and intention of movement for brain computer interface. In: MATEC Web of Conferences. https://doi.org/10.1051/matecconf/201714001028

26.

Krizhevsky A, Sutskever I, Hinton, Geoffrey E (2012) ImageNet classification with deep convolutional neural networks. In: NIPS’12 Proceedings of the 25th international conference on neural information processing systems

27.

Howard A, Menglong Z, Chen B, Kalenichenko D, Wang W, Weyand T et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. Comput Vision Pattern Recognit. arXiv: 1704.04861v1

28.

Hu M (2015) Convolutional neural network for object recognition. In: 2016 23rd international conference on pattern recognition (ICPR) https://doi.org/10.1109/ICPR.2016.7900181

29.

Guo T, Dong J, Li H, Gao Y (2017) Simple convolutional neural network on image classification. In: 2017 IEEE 2nd international conference on big data analysis (ICBDA). https://doi.org/10.1109/ICBDA.2017.8078730

30.

Cecotti H, Graser A (2011) Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2010.125CrossRef

31.

Schirrmeister R, Springenberg J, Fiederer L, Glasstetter M, Eggensperger K, Tangermann M et al (2017) Deep learning with convolutionalneural networks for EEG decoding and visualization. Neural Evolut Comput. https://doi.org/10.1002/hbm.23730CrossRef

32.

Cecotti H, Graeser A (2008) Convolutional Neural Network with embedded Fourier Transform for EEG classification. In: 2008 19th international conference on pattern recognition. https://doi.org/10.1109/ICPR.2008.4761638

33.

Kavasidis I, Palazzo S, Spampinato C, Giordano D, Shah M (2017). Brain2Image: Converting Brain Signals into Images. In: ACM Multimedia 2017. https://doi.org/10.1145/3123266.3127907

34.

Mahaja R, Kaur G (2013) Neural networks using genetic algorithms. Int J Comput Appl 77(14)

35.

Miller G, Todd P (1989) Hegde S. Designing Neural Networks using Genetic Algorithms. In: Proceedings of the 3rd international conference on genetic algorithms

36.

Kim H, Shin K (2007) A hybrid approach based on neural networks andgenetic algorithms for detecting temporal patterns in stock markets. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2006.03.004CrossRef

37.

Schlgl A, Lee F, Bischof H, Pfurtschelle G (2005) Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neuralal Eng. https://doi.org/10.1088/1741-2560/2/4/L02CrossRef

38.

Friedrich E, Scherer R, Neuper C (2012) The effect of distinct mental strategies on classification performance for brain computer interfaces. Int J Psychophysiol. https://doi.org/10.1016/j.ijpsycho.2012.01.014CrossRef

39.

Wang L, Zhang Y, Feng J (2005) On the Euclidean distance of images. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2005.165CrossRef

40.

Chen C, Chu H (2005) Similarity measurement between images. In: 29th Annual International Computer Software and Applications Conference (COMPSAC’05). https://doi.org/10.1109/COMPSAC.2005.140

41.

Müller-Putz G, Scherer R, Brunner C, Leeb R, Pfurtscheller G (2008) Better than random: a closer look on BCI results. Int J Bioelectromagn 10:52–55

42.

Horikawa T, Kamitani Y (2017) Generic decoding of seen and imagined objects using hierarchical visual features. Nat Commun. https://doi.org/10.1038/ncomms15037CrossRef

Title: Classify four imagined objects with EEG signals
Authors: Fabio R. Llorella Costa
Eduardo Iáñez
José M. Azorín
Gustavo Patow
Publication date: 08-03-2021
Publisher: Springer Berlin Heidelberg
Published in: Evolutionary Intelligence / Issue 3/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-021-00577-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2022

Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks

Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

Enhanced statistical nearest neighbors with steerable pyramid transform for Gaussian noise removal in a color image

Hyperspectral image classification using support vector machine: a spectral spatial feature based approach

Minimum landmarks for robot localization in orthogonal environments

Naive bayes-correlation based feature weighting technique for sports match result prediction

Premium Partner