© The Institution of Engineering and Technology
The fast and real-time extraction of precise auditory evoked potential (AEP) is important for the diagnosis of auditory diseases as well as for the brain–computer interface. For fast AEP extraction, the effect of various noises such as motion artefact, eye blink, or power line noise should be minimised in the AEP recording. The existing fast AEP extraction methods that use the Kalman filter or wavelet transform have limitations owing to parametric arbitrariness. In this study, the singular spectrum analysis (SSA) in the time domain was adopted and optimised for AEP extraction with various types of noises. Moreover, the hardware architecture for an optimised SSA was implemented in an FPGA to realise real-time operation. The results show that the optimised SSA method can reduce the stimulus repetition by 61.2% compared with the conventional ensemble averaging and obtain the maximum similarity to the original AEP signal of 83.2%. The designed hardware is favourable for wearable BCI applications in terms of hardware complexity and required clock frequency.
References
-
-
1)
-
9. PhysioNet: ‘Motion artifact contaminated fNIRS and EEG Data’, .
-
2)
-
1. Tangermann, M., Hohne, J., Stecher, H., Schreuder, M.: ‘No surprise-fixed sequence event-related potentials for brain-computer interfaces’. Proc. Annual Int. Conf. IEEE EMBC, San Diego, CA, USA, August 2012, pp. 2501–2504, .
-
3)
-
7. Hassani, H.: ‘Singular spectrum analysis: methodology and comparison’, J. Data Sci., 2007, 5, (2), pp. 239–257.
-
4)
-
2. Wu, Z.W., Yao, M.L., Ma, H.G., Jia, W.M.: ‘De-noising MEMS inertial sensors for lowcost vehicular attitude estimation based on singular spectrum analysis and independent component analysis’, Electron. Lett., 2013, 49, (14), pp. 892–893 (doi: 10.1049/el.2013.0422).
-
5)
-
5. Maddirala, A.K: ‘Removal of EOG artifacts from single channel EEG signals using combined singular spectrum analysis and adaptive noise canceler’, Sens. J., 2016, 16, (23), pp. 8279–8287, .
-
6)
-
2. Ivannikov, A.: ‘Extraction of event related potentials from electroencephalography data’ (Jyvaskyla University Printing House, Seminaarinkatu, Jyvaskyla, Finland, 2009).
-
7)
-
3. Aydin, S.: ‘Comparison of basic linear filters in extracting auditory evoked potentials’, Turk. J. Electr. Eng., 2008, 16, (2), pp. 111–123.
-
8)
-
4. Nurettin, A., Yasemin, E., Yemen, A.B.: ‘Auditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble averaged data’, Neural Comput. Appl., 2013, 22, (5), pp. 859–867, (doi: 10.1007/s00521-011-0776-2).
-
9)
-
8. Ku, Y., Ahn, J.W., Kwon, C., Suh, M.-W., Lee, J.H., Oh, S.H., Kim, H.C.: ‘A programmable acoustic stimuli and auditory evoked potential measurement system for objective tinnitus diagnosis research’. Proc. Annual Int. Conf. IEEE EMBC, Chicago, IL, USA, August 2014, pp. 2749–2752, .
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.1425
Related content
content/journals/10.1049/el.2017.1425
pub_keyword,iet_inspecKeyword,pub_concept
6
6