Decoding Imagined Speech and Computer Control using Brain Waves
نویسندگان
چکیده
In this work, we explore the possibility of decoding Imagined Speech brain waves using machine learning techniques. We propose a covariance matrix Electroencephalogram channels as input features, projection to tangent space matrices for obtaining vectors from matrices, principal component analysis dimension reduction vectors, an artificial feed-forward neural network classification model and bootstrap aggregation creating ensemble models. After classification, two different Finite State Machines are designed that create interface controlling computer system Speech-based BCI system. The proposed approach is able decode signal with maximum mean accuracy 85% on binary task one long word short word. also show our differentiate between imagined speech signals rest state 94%. compared method other approaches performs equivalent art vs. words outperforms it significantly tasks three vowels average margin 11% 9%, respectively. obtain information transfer rate 21-bits-per-minute when IS based operate computer. These results wide variety without any human-designed features.
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ژورنال
عنوان ژورنال: Journal of Neuroscience Methods
سال: 2021
ISSN: ['0165-0270', '1872-678X']
DOI: https://doi.org/10.1016/j.jneumeth.2021.109196