EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition

Journal of Neural Engineering
Aya KhalafMurat Akcakaya

Abstract

We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work. To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences. Average accuracy and average ITR of 98.11% and 21.29 bits m...Continue Reading

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Citations

Apr 18, 2020·Biomedical Engineering Online·Aya Khalaf, Murat Akcakaya
Jul 6, 2021·Journal of Materials Science·Md Mehdi Hasan, Md Milon Hossain
Jul 15, 2021·Journal of Healthcare Engineering·Benzhen GuoZhihui Wang

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