More Web Proxy on the site http://driver.im/

article

Envisioned speech recognition using EEG sensors

Authors:

Rajkumar Saini,

Partha Pratim Roy,

Pawan Kumar Sahu,

Debi Prosad DograAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 22, Issue 1

Pages 185 - 199

https://doi.org/10.1007/s00779-017-1083-4

Published: 01 February 2018 Publication History

Abstract

Recent advances in EEG technology makes brain-computer-interface (BCI) an exciting field of research. BCI is primarily used to adopt with the paralyzed human body parts. However, BCI in envisioned speech recognition using electroencephalogram (EEG) signals has not been studied in details. Therefore, developing robust speech recognition system using EEG signals was proposed. In this paper, we propose a coarse-to-fine-level envisioned speech recognition framework with the help of EEG signals that can be thought of as a serious contribution in this field of research. Coarse-level classification is used to differentiate/categorize text and non-text classes using random forest (RF) classifier. Next, a finer-level imagined speech recognition of each class has been carried out. EEG data of 30 text and not-text classes including characters, digits, and object images have been imagined by 23 participants in this study. A recognition accuracy of 85.20 and 67.03% has been recorded at coarse- and fine-level classifications, respectively. The proposed framework outperforms the existing research work in terms of accuracy. We also show the robustness in envisioned speech recognition.

References

[1]

Brigham Katharine, Vijaya Kumar BVK (2010) Imagined speech classification with EEG signals for silent communication: a preliminary investigation into synthetic telepathy. In: 4th international conference on bioinformatics and biomedical engineering, pp 1---4

[2]

Pineda JA, Allison BZ, Vankov A (2000) The effects of self-movement, observation, and imagination on/spl mu/rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI). IEEE Trans Rehabil Eng 8 (2):219---222

[3]

Jara AJ, Lopez P, Fernandez D, Castillo JF, Zamora MA, Skarmeta AF (2014) Mobile discovery: discovering and interacting with the world through the internet of things. Pers Ubiquit Comput 18(2):323---338

Digital Library

[4]

Han K, Kim J, Shon T, Ko D (2013) A novel secure key paring protocol for rf4ce ubiquitous smart home systems. Pers Ubiquit Comput 17(5):945---949

Digital Library

[5]

Metsis V, Kosmopoulos D, Athitsos V, Makedon F (2014) Non-invasive analysis of sleep patterns via multimodal sensor input. Pers Ubiquit Comput 18(1):19---26

Digital Library

[6]

Pei X, Hill J, Schalk G (2012) Silent communication: toward using brain signals. IEEE Pulse 3(1):43---46

[7]

Kaur B, Singh D, Roy PP A novel framework of EEG-based user identification by analyzing music-listening behavior. Multimedia Tools and Applications 1---22.

Digital Library

[8]

Badcock NA, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G (2013) Validation of the emotiv EPOC®; EEG gaming system for measuring research quality auditory ERPs. PeerJ 1:e38

[9]

Kumar P, Saini R, Roy PP, Dogra DP (2017) A bio-signal based framework to secure mobile devices. J Netw Comput Appl 89:62---71

Digital Library

[10]

Gandhi T, Panigrahi BK, Anand S (2011) A comparative study of wavelet families for EEG signal classification. Neurocomputing 74(17):3051---3057

Digital Library

[11]

Matsumoto M, Hori J (2014) Classification of silent speech using support vector machine and relevance vector machine. Appl Soft Comput 20:95---102

[12]

Houde JF, Nagarajan SS, Sekihara K, Merzenich MM (2002) Modulation of the auditory cortex during speech: an MEG study. J Cogn Neurosci 14(8):1125---1138

Digital Library

[13]

Price CJ (2012) A review and synthesis of the first 20years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage 62(2):816---847

[14]

Kanjo E, Al-Husain L, Chamberlain A (2015) Emotions in context: examining pervasive affective sensing systems, applications, and analyses. Pers Ubiquit Comput 19(7):1197---1212

Digital Library

[15]

Peng H, Bin H, Zheng F, Fan D, Zhao W, Chen X, Yang Y, Cai Q (2013) A method of identifying chronic stress by EEG. Pers Ubiquit Comput 17(7):1341---1347

Digital Library

[16]

Menezes MLR, Samara A, Galway L, SantAnna A, Verikas A, Alonso-Fernandez F, Wang H, Bond R (2017) Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset. Pers Ubiquit Comput 1---11.

Digital Library

[17]

Costa EJX, Cabral EF (2000) EEG-based discrimination between imagination of left and right hand movements using adaptive gaussian representation. Med Eng Phys 22(5):345---348

[18]

DaSalla CS, Kambara H, Sato M, Koike Y (2009) Single-trial classification of vowel speech imagery using common spatial patterns. Neural Netw 22(9):1334---1339

Digital Library

[19]

Parra LC, Spence CD, Gerson AD, Sajda P (2003) Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring. IEEE Trans Neural Syst Rehabil Eng 11 (2):173---177

[20]

D'Zmura M, Deng S, Lappas T, Thorpe S, Srinivasan R (2009) Toward EEG sensing of imagined speech. In: In International Conference on Human-Computer Interaction, pp 40---48

Digital Library

[21]

Li W, Zhang X, Zhong X, Zhang Y (2013) Analysis and classification of speech imagery EEG for BCI. Biomed Signal Process Control 8(6):901---908

[22]

Hsu Y-L, Yang Y-T, Wang J-S, Hsu C-Y (2013) Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 104:105---114

Digital Library

[23]

He SL, Gao X, Yang F, Gao S (2003) Imagined hand movement identification based on spatio-temporal pattern recognition of EEG. In: 1st EMBS conference on neural engineering, pp 599--- 602

[24]

Deng S, Srinivasan R, Lappas T, D'Zmura M (2010) EEG classification of imagined syllable rhythm using hilbert spectrum methods. J Neural Eng 7(4).

[25]

Esfahani ET, Sundararajan V (2012) Classification of primitive shapes using brain---computer interfaces. Comput Aided Des 44(10):1011---1019

Digital Library

[26]

Torres-García AA, Reyes-García CA, Villaseñor-Pineda L, García-Aguilar G (2016) Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification. Expert Systems with Applications 59:1---12

Digital Library

[27]

González-Castañeda EF, Torres-García AA, Reyes-García CA, Villaseñor-Pineda L (2017) Sonification and textification: Proposing methods for classifying unspoken words from EEG signals. Biomed Signal Process Control 37:82---91

[28]

Wang K, Wang X, Li G (2017) Simulation experiment of bci based on imagined speech EEG decoding. arXiv:1705.07771

[29]

Nguyen CH, Karavas G, Artemiadis P (2017) Inferring imagined speech using EEG signals: a new approach using riemannian manifold features J Neural Eng

[30]

Soleymani M, Pantic M, Pun T (2012) Multimodal emotion recognition in response to videos. IEEE Trans Affect Comput 3(2):211---223

Digital Library

[31]

Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B (2017) Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Netw 92:77---88

[32]

Donos C, Dümpelmann M, Schulze-Bonhage A (2015) Early seizure detection algorithm based on intracranial EEG and random forest classification. Int J Neural Syst 25(05):1550023

[33]

Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H (2012) Automated sleep stage identification system based on time---frequency analysis of a single EEG channel and random forest classifier. Comput Methods Prog Biomed 108(1):10---19

Digital Library

[34]

Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinf 10(1):213

[35]

Breiman L (2001) Random forests. Mach Learn 45(1):5---32

Digital Library

[36]

Übeyli ED (2009) Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Process 19(2):297---308

Digital Library

[37]

Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1---3):131---159

Digital Library

[38]

Yadava M, Kumar P, Saini R, Roy PP, Dogra DP (2017) Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications 76(18):19087---19111

Digital Library

[39]

Kumar P, Gauba H, Roy PP, Dogra DP (2017) A multimodal framework for sensor based sign language recognition. Neurocomputing 259:21---38

Cited By

Agarwal PKumar S(2024)EEG-based imagined words classification using Hilbert transform and deep networksMultimedia Tools and Applications10.1007/s11042-023-15664-883:1(2725-2748)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15664-8
Mandal AArisoy CSaxena N(2024)News Reader: A News Interest Identification Attack Using Single-Electrode Brainwave SignalsInformation Security10.1007/978-3-031-75764-8_10(183-202)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75764-8_10
Jaswal GSharma GDutt VBhavsar A(2023)Learning channel attention for decoding of visual imagined text from multi-band EEG using metric learningProceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3594806.3596566(720-727)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3594806.3596566
Show More Cited By

Envisioned speech recognition using EEG sensors
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Communication hardware, interfaces and storage

Recommendations

Predicting epileptic seizures from scalp EEG based on attractor state analysis

The seizure-precursor phenomenon of the epileptic seizure is extracted from an attractor-based analysis of the macroscopic dynamics of the brain.A novel seizure prediction method using scalp electroencephalogram (EEG) based on attractor state analysis ...
Feature Analysis of EEG Based Brain-Computer Interfaces to Detect Motor Imagery
Brain Informatics
Abstract
Brain-Computer Interfaces (BCI) is one of the alluring breakthroughs for mankind as it provides a new way of communication for the patients of neuro-muscular disorders. Electroencephalography (EEG) signals are the most studied type of signals to ...
Connectivity analysis of multichannel EEG signals using recurrence based phase synchronization technique

Real world biological systems such as the human brain are inherently nonlinear and difficult to model. However, most of the previous studies have either employed linear models or parametric nonlinear models for investigating brain function. In this ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 22, Issue 1

February 2018

218 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer-Verlag London Ltd., part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
208
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Agarwal PKumar S(2024)EEG-based imagined words classification using Hilbert transform and deep networksMultimedia Tools and Applications10.1007/s11042-023-15664-883:1(2725-2748)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15664-8
Mandal AArisoy CSaxena N(2024)News Reader: A News Interest Identification Attack Using Single-Electrode Brainwave SignalsInformation Security10.1007/978-3-031-75764-8_10(183-202)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75764-8_10
Jaswal GSharma GDutt VBhavsar A(2023)Learning channel attention for decoding of visual imagined text from multi-band EEG using metric learningProceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3594806.3596566(720-727)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3594806.3596566
Das BKumar PKar DRam SBabu KMohapatra R(2022)A spatio-temporal model for EEG-based person identificationMultimedia Tools and Applications10.1007/s11042-019-07905-678:19(28157-28177)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-019-07905-6
Mishra RSharma KJha RBhavsar A(2022)NeuroGAN: image reconstruction from EEG signals via an attention-based GANNeural Computing and Applications10.1007/s00521-022-08178-135:12(9181-9192)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1007/s00521-022-08178-1
Lee PHwang SJeon SByun H(2021)Subject Adaptive EEG-Based Visual RecognitionPattern Recognition10.1007/978-3-031-02444-3_24(322-334)Online publication date: 9-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-031-02444-3_24
Li RJohansen JAhmed HIlyevsky TWilbur RBharadwaj HSiskind J(2020)The Perils and Pitfalls of Block Design for EEG Classification ExperimentsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.297315343:1(316-333)Online publication date: 3-Dec-2020
https://dl.acm.org/doi/10.1109/TPAMI.2020.2973153
Tirupattur PRawat YSpampinato CShah MBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)ThoughtVizProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240641(950-958)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240641
Alsaleh MMoore RChristensen HArvaneh M(2018)Examining Temporal Variations in Recognizing Unspoken Words Using EEG Signals2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00173(976-981)Online publication date: 7-Oct-2018
https://dl.acm.org/doi/10.1109/SMC.2018.00173

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents