Graph based semantic ensemble of riemannian neural structured learning for BCI-EEG signal...

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Riemannian geometry-based EEG approaches: A literature review

IE Tibermacine, S Russo, A Tibermacine… - arxiv preprint arxiv …, 2024 - arxiv.org
The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs)
has swiftly garnered attention because of its straightforwardness, precision, and resilience …
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GM-VRC: Semantic Topological Data Ensemble Approach for EEG Signal Classification

SD Reddy, TK Reddy - ICASSP 2024-2024 IEEE International …, 2024 - ieeexplore.ieee.org
Usage of Machine Learning (ML) models has been trending for automated screening of
mental health. Electroencephalogram (EEG) signals, due to their non-invasive nature and …
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Chromatic Alpha Complex Generation for EEG Signal Classification

SD Reddy, TK Reddy, H Higashi - 2024 National Conference …, 2024 - ieeexplore.ieee.org
Electroencephalogram (EEG) is high dimensional complex data resembling the electric
conduction of neurons. Ana-lyzing the electric activity and complexity of EEG signals …
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Schizophrenia and Bipolar Psychosis Classification with rsfMRI Functional Connectivity Feature Fusion technique using Super Learner

SD Reddy, K Gaurav, TK Reddy - 2023 IEEE Silchar …, 2023 - ieeexplore.ieee.org
Schizophrenia and bipolar psychosis are intricate mental disorders that share similar clinical
characteristics, making it difficult to establish precise diagnoses and classifications. In recent …
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