Deep Learning pipeline for motor-imagery classification.
It is the task to classify BCI competition datasets (EEG signals) using EEGNet and DeepConvNet with different activation functions. You can get some detailed introduction and experimental results in the link below. https://github.com/secondlevel/EEG-classification/blob/main/Experiment%20Report.pdf
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
An exploratory and educational project on EEG-based emotion recognition using the FACED dataset, with extensive preprocessing, spectral feature extraction, model comparison, and performance analysis, extended to explore recent graph neural networks, pretrained EEG models, and Mamba-based architectures.
Modern EEG-based Lie Detection using P300 Waveform Analysis, Recurrence Plots, and Deep Learning Architectures
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