Proceedings Article10.1109/CSNT57126.2023.10134633
EEG Based Brain Computer Interface System for Decoding Covert Speech using Deep Neural Networks
Meenakshi Bisla,R. S. Anand +1 more
- 08 Apr 2023
pp 414-419
TL;DR: In this paper , a two-dimensional convolutional neural network and LSTM was used to learn deep features from EEG signals corresponding to the imagery of words to design a BCI(Brain computer interface) system based on human thoughts.
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Abstract: The study aims to learn deep features from EEG signals corresponding to the imagery of words to design a BCI(Brain computer interface) system based on human thoughts. Topological plots of time-averaged EEG signals across all the trials corresponding to the imagery of a particular word are fed as input to the designed deep neural network algorithm. Designed deep learning architecture has an amalgamation of the Two-dimensional convolutional neural network and LSTM that takes the assistance of the capabilities and assets of both neural network architectures. The proposed neural network architecture achieves admirable accuracy in identifying imagined words from the EEG-based Kara one dataset. The accuracy of the designed CNN-LSTM model with topological plots as input features was 20-25% more than chance level accuracy and comparable to the state of arts.
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References
Toward EEG Sensing of Imagined Speech
Michael D'Zmura,Siyi Deng,Tom Lappas,Samuel G. Thorpe,Ramesh Srinivasan +4 more
- 14 Jul 2009
TL;DR: Analysis of EEG data from an experiment in which two syllables are spoken in imagination in one of three rhythms shows that information is present in EEG alpha, beta and theta bands.
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Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG.
Pramit Saha,Sidney Fels +1 more
TL;DR: A mixed deep neural network strategy, incorporating parallel combination of Convolutional and Recurrent Neural Networks, cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG demonstrates the promise of a mixed DNN approach for complex spatial-temporal classification problems.
Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.
TL;DR: The co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes, and the auto-calibrating system can be used immediately with a minimal calibration time.