A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images.
Brain–computer interface
Continuous wavelet transform
Deep neural network
Electroencephalogram
P300
Journal
Physical and engineering sciences in medicine
ISSN: 2662-4737
Titre abrégé: Phys Eng Sci Med
Pays: Switzerland
ID NLM: 101760671
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
08
03
2021
accepted:
03
09
2021
pubmed:
23
9
2021
medline:
17
12
2021
entrez:
22
9
2021
Statut:
ppublish
Résumé
Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.
Identifiants
pubmed: 34550551
doi: 10.1007/s13246-021-01057-4
pii: 10.1007/s13246-021-01057-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1221-1230Subventions
Organisme : Department of Biotechnology , Ministry of Science and Technology
ID : REF: NECBH/2019-20/177, BT/COE/34/SP28408/2018
Informations de copyright
© 2021. Australasian College of Physical Scientists and Engineers in Medicine.
Références
Hoffmann U, Vesin J, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167:115–25
doi: 10.1016/j.jneumeth.2007.03.005
Wolpaw JR, Birbaumer N, Mcfarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–91
doi: 10.1016/S1388-2457(02)00057-3
Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng 15:1–28
Blankertz B, Dornhege G, Krauledat M, Muller K, Kunzmann V (2006) The Berlin brain–computer interface EEG-based. IEEE Trans Neural Syst Rehabil Eng 14:147–52
doi: 10.1109/TNSRE.2006.875557
Müller KR, Krauledat M, Dornhege G, Curio G, Blankertz B (2007) Machine learning and applications for brain–computer interfacing. LNCS 4557:1
Felzer T, Freisleben B (2003) Analyzing EEG signals using the probability estimating guarded neural classifier. IEEE Trans Neural Syst Rehabil Eng 11:361–71
doi: 10.1109/TNSRE.2003.819785
Savadkoohi M, Oladunni T, Thompson L (2020) A machine learning approach to epileptic seizure prediction using electroencephalogram (EEG). Signal Biocybern Biomed Eng 40:1328–41
doi: 10.1016/j.bbe.2020.07.004
Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recogn Lett 22:1299–309
doi: 10.1016/S0167-8655(01)00075-7
Hiraiwa A, Shimohara K, Tokunaga Y (1990) EEG topography recognition by neural networks. IEEE Eng Med Biol Mag 9:39–42
doi: 10.1109/51.59211
Liu X, Xie Q, Lv J, Huang H, Wang W (2021) P300 event-related potential detection using one-dimensional convolutional capsule networks. Expert Syst Appl 174:114701
doi: 10.1016/j.eswa.2021.114701
Shukla PK, Chaurasiya RK, Verma S (2021) Performance improvement of P300-based home appliances control classification using convolution neural network. Biomed Signal Process Control 63:102220
doi: 10.1016/j.bspc.2020.102220
Kundu S, Ari S (2020) A deep learning architecture for P300 detection with brain–computer interface. App IRBM 41:31–8
doi: 10.1016/j.irbm.2019.08.001
Farwell L, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–23
doi: 10.1016/0013-4694(88)90149-6
Nidal K, Malik AS (eds) (2014) EEG/ERP analysis: methods and applications. CRC Press, Boca Raton
Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science 150:1187–8
doi: 10.1126/science.150.3700.1187
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B et al (1999) A spelling device for the paralysed. Nature 398:297–8
doi: 10.1038/18581
Pfurtscheller G, Neuper C (2001) Motor imagery direct communication. Proc IEEE 89:1123–34
doi: 10.1109/5.939829
Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H et al (2005) Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurology 64:1775–7
doi: 10.1212/01.WNL.0000158616.43002.6D
Hill NJ, Lal TN, Schröder M, Hinterberger T, Wilhelm B et al (2006) Classifying EEG and ECoG signals without subject training for fast bci implementation: comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans NeuralSyst Rehabil Eng 14:183–6
doi: 10.1109/TNSRE.2006.875548
Croft RJ, Barry RJ (2000) Removal of ocular artifact from the EEG: a review. Neurophysiol Clin 30:5–19
doi: 10.1016/S0987-7053(00)00055-1
Tang P, Wang H, Kwong S (2017) G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225:188–97
doi: 10.1016/j.neucom.2016.11.023
Gaikwad AS, El-Sharkawy M (2018) Pruning convolution neural network (squeezenet) using taylor expansion-based criterion 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018, (IEEE) . vol 1 pp 1–5
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recogn 1:770–8
Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19:4494–500
doi: 10.1109/JSEN.2019.2899645
Cecotti H, Gräser A (2011) Convolutional neural networks for P300 detection with application to brain–computer interfaces. IEEE Trans Pattern Anal Mach Intell 33:433–45
doi: 10.1109/TPAMI.2010.125
Sellers EW, Donchin E (2006) A P300-based brain–computer interface: initial tests by ALS patients. Cl Neurophysiol. 117:538–48
doi: 10.1016/j.clinph.2005.06.027
Piccione F, Giorgi F, Tonin P, Priftis K, Giove S et al (2006) P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol 117:531–7
doi: 10.1016/j.clinph.2005.07.024