Single tap identification for fast BCI control
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One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.
KeywordsBCI DE Feature selection Finger tapping Single trial
The authors would like to thank the reviewers for their many helpful comments which were instrumental in improving the quality of this work. They would also like to extend their thanks to the numerous friends and colleagues who freely gave their time to volunteer as subjects in this study.
- Ang KK (2008) A clinical evaluation of non-invasive motor imagery-based brain-computer interface in stroke. Proc A Int Conf IEEE Eng Med Biol Soc 2008:4178–4181Google Scholar
- Balakrishnan D, Puthusserypady S (2005) Multilayer perceptrons for the classification of brain computer interface data. In: Proceedings of the IEEE 31st annual Northeast bioengineering conference, 2005. IEEE, pp 118–119Google Scholar
- Portelli AJ, Nasuto SJ (2008) Toward construction of an inexpensive brain computer interface for goal oriented applications. In: AISB, Aberdeen, AISB, pp 2–7Google Scholar
- Stavrinou ML, Moraru L, Pelekouda P, Kokkinos V (2006) A wavelet tool to discriminate imagery versus actual finger movements towards a brain computer interface. In: Biological and medical data analysis of 7th international symposium, Thessaloniki, Greece, pp 323–333Google Scholar
- Storn R, Price K (1995) Differential evolution—a simple and effiecient adaptive scheme for global optimzation over continous spacesGoogle Scholar
- Williams N, Daly I, Nasuto SJ, Saddy D, Warwick K (2009) ERP classification using empirical mode decomposition. In: The 5th UK and RI postgraduate conference in biomedical engineering & medical physics, Oxford, pp 5–6Google Scholar