Imagined word pairs recognition from non-invasive brain signals using Hilbert transform
With the advent of new algorithms, brain-computer interfacing has been extensively used in medical and non-medical fields. In this regard, an experiment was conducted by the authors to recognize the imagined speech, the results of which are reported in this paper. This work can act as a speech prost...
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| Published in | International journal of system assurance engineering and management Vol. 13; no. 1; pp. 385 - 394 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New Delhi
Springer India
01.02.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0975-6809 0976-4348 |
| DOI | 10.1007/s13198-021-01283-9 |
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| Abstract | With the advent of new algorithms, brain-computer interfacing has been extensively used in medical and non-medical fields. In this regard, an experiment was conducted by the authors to recognize the imagined speech, the results of which are reported in this paper. This work can act as a speech prosthesis for completely paralyzed patients who cannot communicate normally. Thirteen subjects imagined five English words (sos, stop, medicine, comehere, washroom) while their electroencephalogram (EEG) signals were recorded simultaneously. The word pairs were analyzed in six natural frequencies of the brain. The envelopes of analytical signals acquired from Hilbert transform were calculated for all the frequency bands and the resulting features were classified using seven classifiers. The maximum accuracy reached up to 88.36%. The experimental study showed that alpha and theta frequency bands were able to classify the highest amount of imagined speech with a maximum average accuracy of 72.73% and 69.41% respectively. The results were comparable to state-of-the-art methods. The findings reported in this work will encourage the research community to use non-invasive modalities like EEG for exploring more in this area. |
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| AbstractList | With the advent of new algorithms, brain-computer interfacing has been extensively used in medical and non-medical fields. In this regard, an experiment was conducted by the authors to recognize the imagined speech, the results of which are reported in this paper. This work can act as a speech prosthesis for completely paralyzed patients who cannot communicate normally. Thirteen subjects imagined five English words (sos, stop, medicine, comehere, washroom) while their electroencephalogram (EEG) signals were recorded simultaneously. The word pairs were analyzed in six natural frequencies of the brain. The envelopes of analytical signals acquired from Hilbert transform were calculated for all the frequency bands and the resulting features were classified using seven classifiers. The maximum accuracy reached up to 88.36%. The experimental study showed that alpha and theta frequency bands were able to classify the highest amount of imagined speech with a maximum average accuracy of 72.73% and 69.41% respectively. The results were comparable to state-of-the-art methods. The findings reported in this work will encourage the research community to use non-invasive modalities like EEG for exploring more in this area. |
| Author | Agarwal, Prabhakar Kumar, Sandeep |
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| Cites_doi | 10.1016/S1388-2457(98)00038-8 10.1038/214975a0 10.1097/00001756-199306000-00018 10.1088/1741-2560/7/4/046006 10.1300/J184v03n02_01 10.1109/INDICON47234.2019.9028925 10.1007/s13534-020-00152-x 10.1109/ICASSP.2015.7178118 10.3389/fnins.2020.00290 10.1109/78.782222 10.1016/j.neunet.2009.05.008 10.1109/SPIN48934.2020.9070969 10.1007/978-3-319-47653-7 10.1142/S0219477520500352 10.1038/srep25803 10.1016/j.eswa.2016.04.011 10.1109/TBME.2017.2786251 10.1016/j.neucom.2016.10.024 10.1007/s00779-017-1083-4 10.1023/A:1012450327387 10.1088/1741-2552/aa8235 10.1007/s41133-016-0001-z 10.1109/ICPR.2008.4761297 |
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| Copyright | The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2021 The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2021. |
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| Keywords | Hilbert transform Brain-computer interfacing Imagined speech Electroencephalogram |
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| References | Agarwal P, Kumar S (2021) Transforming imagined thoughts into speech using a covariance-based subset selection method. Indian J Pure Appl Phys 59:180–183. http://nopr.niscair.res.in/handle/123456789/56517 DEWAN EMOccipital alpha rhythm eye position and lens accommodationNature196721497597710.1038/214975a0 Panachakel JT, Ramakrishnan AG, Ananthapadmanabha TV (2019) Decoding imagined speech using wavelet features and deep neural networks. In: 2019 IEEE 16th India council international conference (INDICON). IEEE, Rajkot, India, pp 1–4 DaSallaCSKambaraHSatoMKoikeYSingle-trial classification of vowel speech imagery using common spatial patternsNeural Netw2009221334133910.1016/j.neunet.2009.05.008 Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the thirteenth international conference on machine learning. pp 148–156 Agarwal P, Kale RK, Kumar M, Kumar S (2020) Silent speech classification based upon various feature extraction methods. In: 2020 7th International conference on signal processing and integrated networks (SPIN). IEEE, Noida, India, pp 16–20 DengSSrinivasanRLappasTD’ZmuraMEEG classification of imagined syllable rhythm using Hilbert spectrum methodsJ Neural Eng2010710.1088/1741-2560/7/4/046006 KumarPSainiRRoyPPEnvisioned speech recognition using EEG sensorsPers Ubiquit Comput20182218519910.1007/s00779-017-1083-4 ChapelleOVapnikVBousquetOMukherjeeSChoosing multiple parameters for support vector machinesMach Learn20024613115910.1023/A:10124503273870998.68101 Cooney C, Korik A, Raffaella F, Coyle D (2019) Classification of imagined spoken word-pairs using convolutional neural networks. In: Proceedings of the 8th Graz brain computer interface conference 2019: bridging science and application. Graz University of Technology, Graz, Austria, pp 338–343 MohanchandraKSahaSA communication paradigm using subvocalized speech: translating brain signals into speechAugm Human Res20161310.1007/s41133-016-0001-z Agarwal P, Kumar S, Singh S (2019) Closed form solutions of various window functions in fractional fourier transform domain. In: 2019 6th International conference on computing for sustainable global development (INDIACom). IEEE, New Delhi, India, pp 64–68 MarpleLComputing the discrete-time “analytic” signal via FFTIEEE Trans Signal Process1999472600260310.1109/78.7822220990.94502 Müller-GerkingJPfurtschellerGFlyvbjergHDesigning optimal spatial filters for single-trial EEG classification in a movement taskClin Neurophysiol199911078779810.1016/S1388-2457(98)00038-8 KumarSDirected searching optimization-based speech enhancement techniqueFluct Noise Lett202019205003510.1142/S0219477520500352 QureshiMNIMinBParkHMulticlass classification of word imagination speech with hybrid connectivity featuresIEEE Trans Biomed Eng2018652168217710.1109/TBME.2017.2786251 Torres-GarcíaAAReyes-GarcíaCAVillaseñor-PinedaLGarcía-AguilarGImplementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classificationExpert Syst Appl20165911210.1016/j.eswa.2016.04.011 Siuly S, Li Y, Zhang Y (2016) EEG signal analysis and classification: techniques and applications, 1st edn. Springer Nature, Cham, Switzerland HinkeRMHuXStillmanAEFunctional magnetic resonance imaging of Broca’s area during internal speechNeuroReport1993467567810.1097/00001756-199306000-00018 DashDFerrariPWangJDecoding imagined and spoken phrases from non-invasive neural (MEG) signalsFront Neurosci20201429010.3389/fnins.2020.00290 La VaqueTJThe History of EEG Hans Berger: psychophysiologist. A historical vignetteJ Neurotherapy199931910.1300/J184v03n02_01 MartinSBrunnerPIturrateIWord pair classification during imagined speech using direct brain recordingsSci Rep201662580310.1038/srep25803 HuangNEAttoh-OkineNOThe Hilbert-Huang transform in engineering20051Boca RatonCRC Press1073.44001 Zhao S, Rudzicz F (2015) Classifying phonological categories in imagined and articulated speech. In: 2015 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, South Brisbane, QLD, Australia, pp 992–996 NguyenCHKaravasGKArtemiadisPInferring imagined speech using EEG signals: a new approach using Riemannian manifold featuresJ Neural Eng20171510.1088/1741-2552/aa8235 RamadanRAVasilakosAVBrain computer interface: control signals reviewNeurocomputing2017223264410.1016/j.neucom.2016.10.024 KlemGHLüdersHOJasperHHElgerCThe ten-twenty electrode system of the International Federation. The international federation of clinical neurophysiologyElectroencephalogr Clin Neurophysiol Suppl19995236 Seiffert C, Khoshgoftaar TM, Hulse JV, Napolitano A (2008) RUSBoost: Improving classification performance when training data is skewed. In: 2008 19th International conference on pattern recognition. IEEE, Tampa, FL, USA, pp 1–4 PawarDDhageSMulticlass covert speech classification using extreme learning machineBiomed Eng Lett20201021722610.1007/s13534-020-00152-x D Pawar (1283_CR23) 2020; 10 1283_CR10 RA Ramadan (1283_CR25) 2017; 223 AA Torres-García (1283_CR28) 2016; 59 (1283_CR12) 2005 DEWAN EM (1283_CR9) 1967; 214 1283_CR5 1283_CR3 1283_CR2 O Chapelle (1283_CR4) 2002; 46 1283_CR22 CS DaSalla (1283_CR6) 2009; 22 1283_CR1 1283_CR26 J Müller-Gerking (1283_CR20) 1999; 110 1283_CR27 GH Klem (1283_CR13) 1999; 52 L Marple (1283_CR17) 1999; 47 1283_CR29 S Martin (1283_CR18) 2016; 6 K Mohanchandra (1283_CR19) 2016; 1 CH Nguyen (1283_CR21) 2017; 15 RM Hinke (1283_CR11) 1993; 4 S Deng (1283_CR8) 2010; 7 P Kumar (1283_CR15) 2018; 22 S Kumar (1283_CR14) 2020; 19 D Dash (1283_CR7) 2020; 14 TJ La Vaque (1283_CR16) 1999; 3 MNI Qureshi (1283_CR24) 2018; 65 |
| References_xml | – reference: ChapelleOVapnikVBousquetOMukherjeeSChoosing multiple parameters for support vector machinesMach Learn20024613115910.1023/A:10124503273870998.68101 – reference: KumarSDirected searching optimization-based speech enhancement techniqueFluct Noise Lett202019205003510.1142/S0219477520500352 – reference: PawarDDhageSMulticlass covert speech classification using extreme learning machineBiomed Eng Lett20201021722610.1007/s13534-020-00152-x – reference: DEWAN EMOccipital alpha rhythm eye position and lens accommodationNature196721497597710.1038/214975a0 – reference: HinkeRMHuXStillmanAEFunctional magnetic resonance imaging of Broca’s area during internal speechNeuroReport1993467567810.1097/00001756-199306000-00018 – reference: Müller-GerkingJPfurtschellerGFlyvbjergHDesigning optimal spatial filters for single-trial EEG classification in a movement taskClin Neurophysiol199911078779810.1016/S1388-2457(98)00038-8 – reference: Torres-GarcíaAAReyes-GarcíaCAVillaseñor-PinedaLGarcía-AguilarGImplementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classificationExpert Syst Appl20165911210.1016/j.eswa.2016.04.011 – reference: RamadanRAVasilakosAVBrain computer interface: control signals reviewNeurocomputing2017223264410.1016/j.neucom.2016.10.024 – reference: Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Proceedings of the thirteenth international conference on machine learning. pp 148–156 – reference: MartinSBrunnerPIturrateIWord pair classification during imagined speech using direct brain recordingsSci Rep201662580310.1038/srep25803 – reference: HuangNEAttoh-OkineNOThe Hilbert-Huang transform in engineering20051Boca RatonCRC Press1073.44001 – reference: DaSallaCSKambaraHSatoMKoikeYSingle-trial classification of vowel speech imagery using common spatial patternsNeural Netw2009221334133910.1016/j.neunet.2009.05.008 – reference: Panachakel JT, Ramakrishnan AG, Ananthapadmanabha TV (2019) Decoding imagined speech using wavelet features and deep neural networks. In: 2019 IEEE 16th India council international conference (INDICON). IEEE, Rajkot, India, pp 1–4 – reference: DashDFerrariPWangJDecoding imagined and spoken phrases from non-invasive neural (MEG) signalsFront Neurosci20201429010.3389/fnins.2020.00290 – reference: MarpleLComputing the discrete-time “analytic” signal via FFTIEEE Trans Signal Process1999472600260310.1109/78.7822220990.94502 – reference: Agarwal P, Kale RK, Kumar M, Kumar S (2020) Silent speech classification based upon various feature extraction methods. In: 2020 7th International conference on signal processing and integrated networks (SPIN). IEEE, Noida, India, pp 16–20 – reference: QureshiMNIMinBParkHMulticlass classification of word imagination speech with hybrid connectivity featuresIEEE Trans Biomed Eng2018652168217710.1109/TBME.2017.2786251 – reference: NguyenCHKaravasGKArtemiadisPInferring imagined speech using EEG signals: a new approach using Riemannian manifold featuresJ Neural Eng20171510.1088/1741-2552/aa8235 – reference: Siuly S, Li Y, Zhang Y (2016) EEG signal analysis and classification: techniques and applications, 1st edn. Springer Nature, Cham, Switzerland – reference: Zhao S, Rudzicz F (2015) Classifying phonological categories in imagined and articulated speech. In: 2015 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, South Brisbane, QLD, Australia, pp 992–996 – reference: La VaqueTJThe History of EEG Hans Berger: psychophysiologist. A historical vignetteJ Neurotherapy199931910.1300/J184v03n02_01 – reference: KumarPSainiRRoyPPEnvisioned speech recognition using EEG sensorsPers Ubiquit Comput20182218519910.1007/s00779-017-1083-4 – reference: Agarwal P, Kumar S (2021) Transforming imagined thoughts into speech using a covariance-based subset selection method. Indian J Pure Appl Phys 59:180–183. http://nopr.niscair.res.in/handle/123456789/56517 – reference: DengSSrinivasanRLappasTD’ZmuraMEEG classification of imagined syllable rhythm using Hilbert spectrum methodsJ Neural Eng2010710.1088/1741-2560/7/4/046006 – reference: KlemGHLüdersHOJasperHHElgerCThe ten-twenty electrode system of the International Federation. The international federation of clinical neurophysiologyElectroencephalogr Clin Neurophysiol Suppl19995236 – reference: Agarwal P, Kumar S, Singh S (2019) Closed form solutions of various window functions in fractional fourier transform domain. In: 2019 6th International conference on computing for sustainable global development (INDIACom). IEEE, New Delhi, India, pp 64–68 – reference: MohanchandraKSahaSA communication paradigm using subvocalized speech: translating brain signals into speechAugm Human Res20161310.1007/s41133-016-0001-z – reference: Seiffert C, Khoshgoftaar TM, Hulse JV, Napolitano A (2008) RUSBoost: Improving classification performance when training data is skewed. In: 2008 19th International conference on pattern recognition. IEEE, Tampa, FL, USA, pp 1–4 – reference: Cooney C, Korik A, Raffaella F, Coyle D (2019) Classification of imagined spoken word-pairs using convolutional neural networks. In: Proceedings of the 8th Graz brain computer interface conference 2019: bridging science and application. Graz University of Technology, Graz, Austria, pp 338–343 – volume: 110 start-page: 787 year: 1999 ident: 1283_CR20 publication-title: Clin Neurophysiol doi: 10.1016/S1388-2457(98)00038-8 – volume: 214 start-page: 975 year: 1967 ident: 1283_CR9 publication-title: Nature doi: 10.1038/214975a0 – volume: 4 start-page: 675 year: 1993 ident: 1283_CR11 publication-title: NeuroReport doi: 10.1097/00001756-199306000-00018 – volume: 7 year: 2010 ident: 1283_CR8 publication-title: J Neural Eng doi: 10.1088/1741-2560/7/4/046006 – volume: 3 start-page: 1 year: 1999 ident: 1283_CR16 publication-title: J Neurotherapy doi: 10.1300/J184v03n02_01 – ident: 1283_CR22 doi: 10.1109/INDICON47234.2019.9028925 – ident: 1283_CR1 – ident: 1283_CR5 – volume: 10 start-page: 217 year: 2020 ident: 1283_CR23 publication-title: Biomed Eng Lett doi: 10.1007/s13534-020-00152-x – ident: 1283_CR29 doi: 10.1109/ICASSP.2015.7178118 – volume: 14 start-page: 290 year: 2020 ident: 1283_CR7 publication-title: Front Neurosci doi: 10.3389/fnins.2020.00290 – volume: 47 start-page: 2600 year: 1999 ident: 1283_CR17 publication-title: IEEE Trans Signal Process doi: 10.1109/78.782222 – volume: 22 start-page: 1334 year: 2009 ident: 1283_CR6 publication-title: Neural Netw doi: 10.1016/j.neunet.2009.05.008 – ident: 1283_CR3 doi: 10.1109/SPIN48934.2020.9070969 – ident: 1283_CR27 doi: 10.1007/978-3-319-47653-7 – volume: 52 start-page: 3 year: 1999 ident: 1283_CR13 publication-title: Electroencephalogr Clin Neurophysiol Suppl – volume: 19 start-page: 2050035 year: 2020 ident: 1283_CR14 publication-title: Fluct Noise Lett doi: 10.1142/S0219477520500352 – volume: 6 start-page: 25803 year: 2016 ident: 1283_CR18 publication-title: Sci Rep doi: 10.1038/srep25803 – volume: 59 start-page: 1 year: 2016 ident: 1283_CR28 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.04.011 – volume: 65 start-page: 2168 year: 2018 ident: 1283_CR24 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2017.2786251 – volume: 223 start-page: 26 year: 2017 ident: 1283_CR25 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.10.024 – volume: 22 start-page: 185 year: 2018 ident: 1283_CR15 publication-title: Pers Ubiquit Comput doi: 10.1007/s00779-017-1083-4 – volume-title: The Hilbert-Huang transform in engineering year: 2005 ident: 1283_CR12 – ident: 1283_CR10 – ident: 1283_CR2 – volume: 46 start-page: 131 year: 2002 ident: 1283_CR4 publication-title: Mach Learn doi: 10.1023/A:1012450327387 – volume: 15 year: 2017 ident: 1283_CR21 publication-title: J Neural Eng doi: 10.1088/1741-2552/aa8235 – volume: 1 start-page: 3 year: 2016 ident: 1283_CR19 publication-title: Augm Human Res doi: 10.1007/s41133-016-0001-z – ident: 1283_CR26 doi: 10.1109/ICPR.2008.4761297 |
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| Title | Imagined word pairs recognition from non-invasive brain signals using Hilbert transform |
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