Key result
A novel classification technique using Tunable Q-Wavelet Transform decomposition combined with a deep neural network and support vector machine achieved 100% accuracy in identifying brain states.
Why the study?
Brain-computer interfaces simplify communication for physically disabled individuals suffering from severe brain injury or stroke who have lost the ability to speak.
Does TQWT decomposition combined with SVM or DNN improve classification accuracy of EEG signals for brain state identification?
Does TQWT decomposition combined with SVM or DNN improve classification accuracy of EEG signals for brain state identification?
TQWT decomposition combined with SVM or DNN achieves near-perfect accuracy in classifying EEG signals for brain-computer interfaces.
Answers are grounded in this paper and cite their sources.
Caution against clinical adoption; extends ML methods for EEG brain-state classification.
Brain-Computer Interface provides and simplifies the communication channel for the physically disabled individuals suffering from severe brain injury related to brain stroke and lost ability to speak. It helps these patients to connect with the outside world. In the proposed work, the electroencephalogram signal is used as an input source taken from Bonn University database that is divided into three class of data consisting of 247 samples each. It is further processed by Tunable Q-Wavelet Transform signal decomposition technique where the signals are subdivided into various sub-bands depending on the value of Q-factor, redundancy factor, and number of sub-bands. A novel custom technique uses Q-factor of 3, redundancy value of 3 & 12 number of sub-bands for high pass filtering as well as Q-factor of 1, redundancy value of 3 & 7 number of sub-bands for low pass filtering combined with nine statistical measures for feature extraction purpose. The classification is performed by using multi-class support vector machine giving the accuracy of 99.59%. The accuracy performs best when compared with the existing research results Furthermore, the comparative study has been performed on the same dataset by using deep neural network along with support vector machine giving an accuracy of 100%. Other evaluation parameters such as precision, sensitivity, specificity, and F1 score are also calculated. The classified data help transform the signal into three communication messages that will help solve the speech impairment of disabled individuals.
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Agrawal et al. (2021) studied Speech impairment due to severe brain injury or stroke (n=741). Tunable Q-Wavelet Transform (TQWT) decomposition with deep neural network and support vector machine vs. Existing research results was evaluated on Classification accuracy. A novel classification technique using Tunable Q-Wavelet Transform decomposition combined with a deep neural network and support vector machine achieved 100% accuracy in identifying brain states.
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