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PhD Thesis Colloquium of Jerrin Thomas Panachakel
June 6, 2022 @ 2:30 pm - 3:30 pm UTC+0
Title of the thesis: Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG
Date and Time: June 6, 2022 (Monday) 2.30 PM
Microsoft Teams meeting link:
Abstract: The thesis explores several architectures for accurately decoding the cognitive activity from EEG recorded during speech imagery and Rajayoga meditation. The major contributions of the thesis are listed below:
Neural Correlates of Phonological Category in Speech Imagery EEG
- We have shown that neural correlates of phonological categories exist in the EEG recorded during speech imagery. These correlates lead to significant differences in the mean phase coherence (MPC) values of the EEG across several cortical regions.
- We have also shown that MPC values can be used for accurately classifying the EEG recorded during speech imagery based on the phonological category of the prompts. The proposed architecture for this task has an accuracy of 84.9%.
Decoding Imagined Words from EEG
- One of the challenges in designing systems for decoding imagined words from EEG is the limited availability of data. We have presented three architectures for decoding imagined words from EEG. All three architectures alleviate this problem of limited availability of data.
- The transfer learning-based architecture employs MPC and magnitude-squared coherence values along with a ResNet50-based classifier. This architecture achieves an accuracy of 92.8% on a publicly available EEG dataset in classifying speech imagery.
Classification of Altered State of Consciousness from Resting State
- We have presented three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state.
- Both CSP-LDA-LSTM (common spatial pattern-linear discriminant analysis-long short-term memory) and SVD-DNN (singular value decomposition-deep neural network) architectures are able to capture subject-invariant features.
- The best intra-subject accuracy obtained is 98.2% and the best inter-subject accuracy is 96.4%.
Publications from the Thesis:
- Panachakel, Jerrin Thomas, and Ramakrishnan Angarai Ganesan. “Decoding Imagined Speech From EEG Using Transfer Learning.” IEEE Access 9 (2021): 135371-135383.
- Panachakel, Jerrin Thomas, and Angarai Ganesan Ramakrishnan. “Decoding covert speech from EEG – a comprehensive review.” Frontiers in Neuroscience (2021): 392.
- Panachakel, Jerrin Thomas, et al. “Can we identify the category of imagined phoneme from EEG?.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.
- Panachakel, Jerrin Thomas, and Ramakrishnan Angarai Ganesan. “Classification of phonological categories in imagined speech using phase synchronization measure.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.
- Panachakel, Jerrin Thomas, Ramakrishnan Angarai Ganesan, and T. V. Ananthapadmanabha. “Decoding imagined speech using wavelet features and deep neural networks.” 2019 IEEE 16th India Council International Conference (INDICON). IEEE, 2019.
- Panachakel, Jerrin Thomas, Ramakrishnan Angarai Ganesan, and T. V. Ananthapadmanabha. “Common Spatial Pattern Based Data Augmentation Technique for Decoding Imagined Speech.” 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021.
- Panachakel, Jerrin Thomas, et al. “Binary classification of meditative state from the resting state using EEG.” 2021 IEEE 18th India Council International Conference (INDICON). IEEE, 2021.
- Panachakel, Jerrin Thomas, et al. “Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM.” TENCON 2021-2021 IEEE Region 10 Conference (TENCON). IEEE, 2021.
ALL ARE WELCOME!