Abstract
Neural interfaces are developing worldwide owing to the wide spectrum of possible applications, ranging from military to entertainment industry. Future human-machine interaction (HMI) and brain-machine interfaces (BMI) are inconceivable without further development in the field. Possible applications can also be found in traffic, i.e. remote supervision and/or control of vehicles/ships. In order to obtain effective BMI, two tasks should be performed: the calculation of the EM field by numerical methods (FEM (Finite Elements Method), BEM (Boundary Elements Method), and hybrid) and signal analysis, which should tell the machine what is required (command understanding). Examples of brain signal analysis using FFT and various WT transforms are presented. The basics of numerical methods for topic application are also covered. EEG data are obtained by experiments on consenting human subjects. Finally, the implications of neural interface development and introduction to traffic applications are considered.
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References
Vidaković MR, Jerlović A, Jurić T, Vujović I, Šoda J, Erceg N, Bubić A, Schönwald MZ, Liumis P, Gabelica D, Đogaš Z (2016) Neurophysiologic markers of primary motor cortex for laryngeal muscles and premotor cortex in caudal opercular part of inferior frontal gyrus investigated in motor speech disorder: a navigated transcranial magnetic stimulation (TMS) study. Cogn Process 17:429–443
Kocadagli O, Langari R (2017) Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Syst Appl 88:419–434
Patidar S, Panigrahi T (2017) Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control 34:74–80
Vidaković MR, Gabelica D, Vujović I, Šoda J, Batarelo N, Džimbeg A, Schönwald MZ, Rotim K, Đogaš Z (2015) A novel approach for monitoring writing interferences during navigated transcranial magnetic stimulation mappings of writing related cortical areas. J Neurosci Meth 255:139–150
Mousaa FA, El-Khoribib RA, Shomanb ME (2016) A novel brain computer interface based on principle component analysis. Procedia Comput Sci 82:49–56
Gandhi V, Prasad G, Coyle D, Behera L, McGinnity TM (2015) Evaluating quantum neural network filtered motor imagery brain-computer interface using multiple classification techniques. Neurocomputing 170:161–167
Cecílio J, Andrade J, Martins P, Castelo-Branco M, Furtado P (2016) BCI framework based on games to teach people with cognitive and motor limitations. Procedia Comput Sci 83:74–81
Vukšić M, Josipović S, Čorić A, Kraljevića A (2017) Underwater ROV as inspection and development platform. Trans Marit Sci. https://doi.org/10.7225/toms.v06.n01.005
Mudronja L, Matić P, Katalinić M (2017) Data-based modelling of significant wave height in the adriatic sea. Trans Marit Sci. https://doi.org/10.7225/toms.v06.n01.001
Bennakhi A, Safar M, Abdulrasoul J (2017) Homonoia: when your car reads your mind. Procedia Comput Sci 110:135–142
Ganin IP, Shishkin SL, Kaplan AY (2013) A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design. PLoS ONE 8. https://doi.org/10.1371/journal.pone.0077755
Mak JN, Arbel Y, Minett JW, McCane LM, Yuksel B et al (2011) Optimizing the P300-based brain-computer interface: current status, limitations and future directions. J Neural Eng. https://doi.org/10.1088/1741-2560/8/2/025003
Vujović I, Šoda J, Kuzmanić I (2012) Cutting-edge mathematical tools in processing and analysis of signals in marine and navy. Trans Marit Sci. https://doi.org/10.7225/toms.v01.n01.005
Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22:123–151
Poljak D, Cavka D, Dodig H, Peratta C, Peratta A (2014) On the use of the boundary element analysis in bioelectromagnetics. Eng Anal Bound Elem 49:2–14
Dodig H, Poljak D, Peratta A (2012) Hybrid BEM/FEM edge element computation of the thermal rise in the 3D model of the human eye induced by high frequency EM waves. In: 20th international conference on software, telecommunications and computer networks (SoftCOM), pp 1–5
Dodig H, Cvetkovic M, Poljak D, Hirata A, Laakso I (2017) Hybrid FEM/BEM for human head exposed to high frequency electromagnetic radiation. In: 40th international conference on boundary elements and other mesh reduction methods, Brockenhurst, UK, pp 239–250, 12–14 Sept 2017
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Kuzmanić, I., Vujović, I., Šoda, J., Rogić Vidaković, M. (2020). Signal Analysis and Numerical Method Roles in Neural Interfaces Development. In: Öchsner, A., Altenbach, H. (eds) Engineering Design Applications II. Advanced Structured Materials, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-030-20801-1_16
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DOI: https://doi.org/10.1007/978-3-030-20801-1_16
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