TY - GEN
T1 - Tracing and decoding of covert phonemes using single channel Electroencephalogram with Machine Learning Techniques
AU - Perumal, Varalakshmi
AU - Medikanda, Jeevan
N1 - Funding Information:
Special mention to all the volunteers who participated in this study with all their interest and willingness. We would like to thank Dr Muralidhar G. Bairy, Head of the department, Department of Biomedical Engineering, Manipal Institute of Technology, Manipal and Department of Biomedical Engineering for their support throughout.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A Brain-computer interface BCI is a technology that interfaces the brain and computer for communication without the person expressing it. Amongst concepts of reading thoughts of the brain, decoding covert speech is a popular application in BCI which can be able to translate the imagined voice inside a person. In this study, Electroencephalogram (EEGs) has been used to interpret the covert speech of a person. On the other hand, reading the brain with EEG is a complicated task to use in daily life applications as it needs multichannel spatial information to be extracted by connecting leads all over the scalp. In the direction of overcoming this complexity, this study uses only single-channel EEG Fpz, which is much easier to access than channels. In this study, Multilayer Perceptron (MLP), K-nearest neighbour Classifier (KNN), Support Vector Classifier (SVC), and Random Forest (RF) models are proposed to classify a single channel Fpz of EEG by extracting spectral information in form of wavelet decomposition coefficients and an energy level over Alpha, Beta, Gamma, Delta and Theta bands to show the evidence that covert speech can be derived through single channel EEG with basics classifiers.
AB - A Brain-computer interface BCI is a technology that interfaces the brain and computer for communication without the person expressing it. Amongst concepts of reading thoughts of the brain, decoding covert speech is a popular application in BCI which can be able to translate the imagined voice inside a person. In this study, Electroencephalogram (EEGs) has been used to interpret the covert speech of a person. On the other hand, reading the brain with EEG is a complicated task to use in daily life applications as it needs multichannel spatial information to be extracted by connecting leads all over the scalp. In the direction of overcoming this complexity, this study uses only single-channel EEG Fpz, which is much easier to access than channels. In this study, Multilayer Perceptron (MLP), K-nearest neighbour Classifier (KNN), Support Vector Classifier (SVC), and Random Forest (RF) models are proposed to classify a single channel Fpz of EEG by extracting spectral information in form of wavelet decomposition coefficients and an energy level over Alpha, Beta, Gamma, Delta and Theta bands to show the evidence that covert speech can be derived through single channel EEG with basics classifiers.
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U2 - 10.1109/DISCOVER55800.2022.9974955
DO - 10.1109/DISCOVER55800.2022.9974955
M3 - Conference contribution
AN - SCOPUS:85145354646
T3 - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
SP - 320
EP - 324
BT - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Y2 - 14 October 2022 through 15 October 2022
ER -