Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

By Hubert Cecotti, Axel Gräser. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

747 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/226ef5e8f58daece22b474ecf69308d87576f36b

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020, background): “However, these approaches either still involve in complex preprocessing steps, like converting EEG signals to images [17], or neglect the subtle spatial and temporal information contained within EEG signals [18], [19].”
3D Convolutional Neural Networks for Human Action Recognition (2010, neutral): “…competitive performance in visual object recognition [17], [19], [22]–[24], human action recognition [25]–[27], natural language processing [28], audio classification [29], brain-computer interaction [30], human tracking [31], image restoration [32], denoising [33], and segmentation tasks [34].”
Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification (2017, methodology): “In the deep learning process, a feature map represents a layer entity, which means that each map of the prior hidden layer is a channel combination, while the following hidden layer subsamples and transforms the signal in the time domain [30].”
Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients (2018, methodology): “The first framework is to use deep neural networks as a feature extractor that is combined with discriminative/generative models [18], [19], [21], [24], and the other framework is to use an entire neural network-based classification pipeline [22], [29].”
EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation (2019, neutral): “recognition [24], P300 detection [25], effective radiated power classification [26], and memory performance prediction [27].”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “quency-transformed EEG signals for classication among dierent classes of listened music [34,38]. Temporal convolutional networks have also been used for the detection of visual-evoked potentials in [39] and in [33]. However, a comprehensive analysis of the performance of a single model architecture ”
Deep learning in bioinformatics (2016, neutral): “[167] classified characters that the participants viewed.”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, methodology): “One representative work of brain-controlled manipulators is the manipulator used within the FRIEND system developed by Graser et al. [143], [144], which is able to show the brain-controlled capabilities of robots out of a controlled laboratory situation.”
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks (2015, methodology): “In other works, convolutional and recurrent neural networks have been used to extract representations from EEG time series (Mirowski et al., 2009; Cecotti & Gr¨aser, 2011; Guler et al., 2005).”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “tion flows unidirectionally from the input to the convolution operator and then to the output [92].”
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks (2018, methodology): “Cecotti and Graser [23] introduced a CNN classifier for classification of a P300 (a positive peak seen in the EEG signal 300 ms after presenting a stimulus to the subject) speller task [23].”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, background): “Yet, this constitutes a degree of freedom one may play with to increase the performances of the network as was explored in [26].”
Deep Learning Human Mind for Automated Visual Classification (2016, background): “All subjects were homogeneous in terms of age, education level and cultural background and were evaluated by a professional physicist in order to exclude possible conditions (e.g., diseases) interfering with the acquisition process.”
Deep Feature Learning for EEG Recordings (2015, methodology): “Another CNN for detecting P300 waves (a well established waveform in EEG research) was described in Cecotti & Gr¨aser (2011).”