Machine learning techniques for brain signal analysis with applications on seizure detection and brain-computer interfaces

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Electroencephalographic (EEG) and electrocorticographic (ECoG) data have been widely used for brain signal analysis in the diagnosis of brain diseases and in the field of braincomputer interface (BCI). Our research emphasizes the role of machine learning (ML) techniques in feature extraction and classification of EEG/ECoG signals, where we primarily focus on their applications on epileptic seizure detection and translation of brain activities into control commands for BCI system.