Deep Learning with Convolutional Neural Networks

By William B. Claster. Mathematics and Programming for Machine Learning with R, 2020

301 citations recorded by the source corpus.

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BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, background): “While a good deal of evidence (Schirrmeister et al., 2017; Chambon et al., 2018; Lawhern et al., 2018; Lotte et al., 2018; Kostas et al., 2019; Kostas and Rudzicz, 2020b) supports the advantage of temporally focused stages (no EEG channel mixing) separate from a stage (or more) that integrates chann”
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization (2022, methodology): “As expected, the modiﬁed CNN model, ConvNet [16], achieves comparable performance to traditional algorithms on EEG classiﬁcation tasks, learning discriminative features in convolutional layers.”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, unknown): “[184] decoded hand movements from EMG signals.”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “was shown to outperform FBCSP on BCI Competition 2A [77].”