Towards optimal visual presentation design for hybrid EEG-fTCD brain-computer interfaces

Journal of Neural Engineering
Aya KhalafMurat Akcakaya

Abstract

In this paper, we introduce a novel hybrid brain-computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy. Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes. EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3...Continue Reading

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Citations

Mar 1, 2019·Journal of Neural Engineering·Aya KhalafMurat Akcakaya
Apr 18, 2020·Biomedical Engineering Online·Aya Khalaf, Murat Akcakaya

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