References
- King CE, Wang PT, McRimmon CM, et al. The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. [updated 2015 Aug; cited 2018 Apr 9]. Available from: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-015-0068-7.
- Dokkum LEHV, Ward T, Laffont I. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. [updated 2015 Feb; cited 2018 Apr 12]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25614021.
- B.C.A O. Neurorehabilitation of hand functions using Brain-Computer Interface. [updated 2015 Sep; cited 2018 Apr 12]. Available from: http://theses.gla.ac.uk/7245.
- Irimia DC, Poboroniuc MS, Serea F, et al. Controlling a FES-EXOSKELETON Rehabilitation System by Means of Brain-Computer Interface. [updated 2016 Oct; cited 2018 Apr 15]. Available from: https://ieeexplore.ieee.org/document/7781361.
- Liberson WT, Holmquest HJ, Scot D, et al. Functional electrotherapy: stimulation of the peroneal nerve synchronized with the swing phase of the gait of hemiplegic patients. [updated 1961 Feb; cited 2018 May 30]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/13761879.
- Moe JH, Post HW. Functional electrical stimulation for ambulation in hemiplegia. [updated 1962 Jul; cited 2018 May 30]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/14474974.
- Pfurtscheller G, Muller GR, Pfurtscheller J, et al. “Thought” - control of a functional electrical stimulation to restore hand grasp in a patient with tetraplegia. [updated 2003 Nov; cited 2018 Apr 24]. Available from: https://www.sciencedirect.com/science/article/pii/S0304394003009479.
- Do AH, Wang PT, King CE, et al. Brain-computer interface controlled functional electrical stimulation system for ankle movement. [cited 2018 Apr 24]. Available from: https://jneuroengrehab.biomedcentral.com/track/pdf/10.1186/1743-0003-8-49.
- Badia SB, Morgade AG, Samaha H, et al. Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training. [updated 2012 Nov; cited 2018 Apr 24]. Available from: https://ieeexplore.ieee.org/abstract/document/6363609.
- Pietro C, Silvia S, Federica P, et al. NeuroVirtual 3D: a multiplatform 3D simulation system for application in psychology and neuro-rehabilitation. [updated 2014 Apr; cited 2018 Apr 24]. Available from: https://link.springer.com/chapter/10.1007%2F978-3-642-54816-1_15.
- Ruiz AF, Arturo FC, Rocon E, et al. Exoskeletons for rehabilitation and motor control. [updated 2006 Feb; cited 2018 Apr 24]. Available from: https://ieeexplore.ieee.org/abstract/document/1639155.
- Cooley JW, Tukey JW. An algorithm for the machine calculation of complex fourier series. Mathematics of computation, Vol. 19, No. 90. [updated 1965 Apr; cited 2018 Jun 4]. Available from: https://www.jstor.org/stable/2003354?seq=1#page_scan_tab_contents.
- Cochran WT, Cooley JW, Favin DL, et al. What is the fast fourier transform. Proceedings of the IEEE, Vol. 55, No. 10, pp. 1664–1674. [updated 1967 Oct; cited 2018 Jun 4]. Available from: https://ieeexplore.ieee.org/document/1447887.
- Al-Fahoum AS, Al-Fraihat AA. Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN neuroscience. [updated 2014; cited 2018 Jun 4]. Available from: https://www.hindawi.com/journals/isrn/2014/730218.
- Solomon. PSD computations using Welch’s method. [updated 1991; cited 2018 Jun 4]. Available from: https://www.osti.gov/servlets/purl/5688766.
- DAUBECHIES. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theory. 1990;36(5):961–1005.
- Soltani S. On the use of the wavelet decomposition for time series prediction. Neurocomputing. 2002;48(1–4):267–277.
- Prochazka , J Kukal, O. Vysata Wavelet transform use for feature extraction and EEG signal segments classification. Proceedings of the 3rd international symposium on communications, control and signal processing (ISCCSP ‘08), pp. 719–722. [updated 2008 Mar; cited 2018 Jun 10]. Available from: https://ieeexplore.ieee.org/document/4537317.
- Stoica P, Nehorai A. Study of the statistical performance of the Pisarenko harmonic decomposition method. IEE Proc F Commun Radar Signal Process UK. 1988;135(2):161–168.
- Awang SA, Paulraj MP, Yaacob S. Analysis of EEG signals by Eigenvector methods. Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES ‘12). 2012:778–783. [cited 2018 Jun 10]. Available from: https://ieeexplore.ieee.org/document/6498164.
- Übeyli ED. Analysis of EEG signals by implementing Eigenvector methods/recurrent neural networks. Digital Signal Processing. 2009;19(1):134–143.
- Übeyli ED. Analysis of EEG signals by combining Eigenvector methods and multiclass support vector machines. Comput Biol Med. 2008;38(1):14–22.
- Jatupaiboon N, Pan-Ngum S, Israsena P. Real-time EEG-based happiness detection system. Sci World J. 2013;2013:1.
- Li M, Zhang M, Luo X, et al. Combined long short-term memory based network employing wavelet coefficients for MI-EEG Recognition. Proceedings of the 2016 IEEE international conference on mechatronics and automation (ICMA ‘16). [updated 2016 Aug; cited 2018 Jun 112]. Available from: https://ieeexplore.ieee.org/document/7558868.
- Hefron RG, Borghetti BJ, Christensen JC, et al. Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation. Pattern Recog Lett. 2017;94:96–104.
- Chapman RM, Bragdon HR. Evoked responses to numerical and non-numerical visual stimuli while problem solving. Nature. 1964;203(4950):1155–1157.
- Fazel-Rezai R, Allison BZ, Guger C, et al. P300 brain computer interface: current challenges and emerging trends. Front Neuroeng. 2012;5:14.
- Fazel-Rezai R, Gavett S, Ahmad W, et al. A comparison among several P300 brain-computer interface speller paradigms. Clin EEG Neurosci. 2011;42(4):209.
- Pan J, Li Y, Gu Z, et al. A comparison study of two P300 speller paradigms for brain-computer interface. Cogn Neurodyn. 2013;7(6):523–529.
- David R. Evoked potentials in psychology, sensory physiology and clinical medicine. Netherlands: Springer; 1972.
- Sharbrough F, Chatrian GE, Lesser R, et al. American electroencephalographic society guidelines for standard electrode position nomenclature. J Clin Neurophysiol. 1991;8(2):200–202.
- Kuo FF, Kaiser JF. System analysis by digital computer. New York: John Wiley and Sons; 1966.
- Butterworth S. On the theory of filter amplifiers. Experimental Wireless and Wireless Engineer. 1930;7:536–541.
- Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
- Quinlan JR. Induction of decision tress. Mach Learn. 1986;1(1):81–106.
- Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
- Brooke J. SUS – A quick and dirty usability scale. Usability evaluation in industry. [cited 2018 Aug 31]. Available from: https://hell.meiert.org/core/pdf/sus.pdf.
- Sauro J. Does prior experience affect oerceptions of usability. [updated 2011; cited 2019 May 14]. Available from: http://www.measuringu.com/blog/prior-exposure.php.
- Sauro J. Measuring usability with the System Usability Scale (SUS). [updated 2011; cited 2019 May 14]. Available from: http://www.measuringu.com/blog/sus.php.
- Sauro J. SUStisfied? Little-known system usability scale facts. [updated 2011; cited 2019 May 14]. Available from: http://uxpamagazine.org/sustified/.