Abstract
The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-computer interface (BCI) technology could be an effective alternative to rehabilitate their hearing ability. In this paper, the AEP responses of three distinct English words namely bed, please and sad have been recognized. The EEG features in terms of Fast Fourier Transform (FFT), power spectral density (PSD), spectral centroids, standard deviation, Log energy entropy, mean, skewness, kurtosis has been selected as a feature extraction method. Support Vector Machine (SVM), Linear discriminant analysis (LDA) and K-Nearest Neighbors (K-NN) have been employed to classify the extracted features. Among all these features, power spectral density with SVM classification has achieved the best accuracy. Different performance measures were evaluated to identify the best set of features as well as model. The best classification accuracy was demonstrated by the developed SVM model was observed as 82.86% which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.
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References
Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 4. https://doi.org/10.1088/1741-2560/4/2/R03
Plourde G (2006) Auditory evoked potentials. Best Pract Res Clin Anaesthesiol 20:129–139. https://doi.org/10.1016/j.bpa.2005.07.012
Grönfors T (1993) Peak identification of auditory brainstem responses with multifilters and attributed automaton. Comput Methods Programs Biomed 40:83–87. https://doi.org/10.1016/0169-2607(93)90002-3
Boston JR (1989) Automated interpretation of brainstem auditory evoked potentials: a prototype system. IEEE Trans Biomed Eng 36:528–532. https://doi.org/10.1109/10.24254
Vannier E, Adam O, Motsch JF (2002) Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels. Artif Intell Med 25:283–301. https://doi.org/10.1016/S0933-3657(02)00029-5
Popescu M, Papadimitriou S, Karamitsos D, Bezerianos A (1999) Adaptive denoising and multiscale detection of the V wave in brainstem auditory evoked potentials. Audiol Neuro-Otology 4:38–50. https://doi.org/10.1159/000013818
Phatak S, Allen J (2006) Consonant and vowel confusions in speech-weighted noise. In: INTERSPEECH 2006 9th international conference on spoken language processing. INTERSPEECH 2006—ICSLP, vol 3, pp 1515–1518. https://doi.org/10.1121/1.2642397
Billings CJ, Bennett KO, Molis MR, Leek MR (2012) Cortical encoding of signals in noise: effects of. 32:53–60. https://doi.org/10.1097/AUD.0b013e3181ec5c46.CORTICAL
Tan L, Chen Y, Maloney TC, Caré MM, Holland SK, Lu LJ (2013) Combined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment. NeuroImage Clin 3:416–428. https://doi.org/10.1016/j.nicl.2013.09.008
Mosqueda Cárdenas E, de la Rosa Gutiérrez JP, Aguilar Lobo LM, Ochoa Ruiz G (2019) Automatic detection and classification of hearing loss conditions using an artificial neural network approach. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Springer Verlag, pp. 227–237. https://doi.org/10.1007/978-3-030-21077-9_21
Moinnereau M-A, Whittingstall K, Plourde E (2019) Electroencephalogram (EEG) recordings obtained when simultaneously presenting audio stimulations. IEEE Dataport. http://dx.doi.org/10.21227/e90n-sa08
Goyal D, Choudhary A, Pabla BS, Dhami SS (2020) Support vector machines based non-contact fault diagnosis system for bearings. J Intell Manuf 31:1275–1289. https://doi.org/10.1007/s10845-019-01511-x
Rashid M, Sulaiman N, Mustafa M, Khatun S, Bari BS, Hasan MJ, Al-Fakih NMMA (2020) Investigating the possibility of brain actuated mobile robot through single-channel EEG headset. Lect Notes Electr Eng 632:579–590. https://doi.org/10.1007/978-981-15-2317-5_49
AydIn S, Saraoǧlu HM, Kara S (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng 37:2626–2630. https://doi.org/10.1007/s10439-009-9795-x
Hortal E, Iáñez E, Úbeda A, Planelles D, Costa Á, Azorín JM (2014) Selection of the best mental tasks for a SVM-based BCI system. In: Proceedings of IEEE International conference on systems, man, and cybernetics. pp 1483–1488 (2014-January). https://doi.org/10.1109/smc.2014.6974125
Rashid M, Sulaiman N, Mustafa M, Bari BS, Sadeque MG, Hasan MJ (2020) Wink based facial expression classification using machine learning approach. SN Appl Sci 2. https://doi.org/10.1007/s42452-020-1963-5
Cui G, Zhao Q, Cao J, Cichocki A (2014) Hybrid-BCI: classification of auditory and visual related potentials. In: 2014 joint 7th international conference on soft computing and intelligent systems, SCIS 2014 and 15th international symposium on advanced intelligent systems, ISIS 2014, pp 297–300. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SCIS-ISIS.2014.7044768
Bose R, Khasnobish A, Bhaduri S, Tibarewala DN (2016) Performance analysis of left and right lower limb movement classification from EEG. In: 3rd international conference on signal processing and integrated networks, SPIN 2016, pp 174–179. https://doi.org/10.1109/SPIN.2016.7566683
Rashid M, Sulaiman N, Mustafa M, Jadin MS, Najib MS, Bari BS, Khatun S (2020) Analysis of EEG features for brain computer interface application. Lect Notes Electr Eng 632:529–540. https://doi.org/10.1007/978-981-15-2317-5_45
Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. Northeast SAS Users Gr. 2010 health care and life science, pp 1–9
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The author would like to acknowledge Universiti Malaysia Pahang for providing FRGS to support this research via RDU190109.
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Islam, M.N., Sulaiman, N., Rashid, M., Mustafa, M., Jadin, M. (2022). Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_45
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