Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization
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Abstract
OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process. APPROACH: We developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output. MAIN RESULTS: The CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level = 8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task. SIGNIFICANCE: Following systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.
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The LOSO strategy was more likely to enhance input samples relevant to the decoding task compared with WS/CS trainings.
“For each dataset, these representations were computed using MS-EEGNet trained with the LOSO strategy, as this strategy was more likely to enhance input samples relevant to the decoding task compared with WS/CS trainings (Farahat et al., 2019).”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Farahat et al., 2019
The Saliency maps section (page unavailable) says cross-subject models are less likely than within-subject models to pick up task-irrelevant, subject-specific features. The Cross-validation schemes section describes LOSO, but the paper does not establish that LOSO enhances task-relevant inputs more than both WS and CS training; its saliency-map comparison uses a different cross-subject training setup. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFarahat et al. (2019) proposed a dual-branched CNN (BranchedNet) that learns temporal features in two different timescales with parallel temporal convolutions, reporting an increase in performance with respect to a single-scale convolution.
“Farahat et al. (2019) proposed a dual-branched CNN (BranchedNet) that learns temporal features in two different timescales with parallel temporal convolutions, reporting an increase in performance with respect to a single-scale convolution.”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Farahat et al., 2019
Saliency maps have been recently transposed to P300 decoding.
“saliency maps (Simonyan et al., 2013), simple representations reporting the gradient of a target class score with respect to each input pixel, have been recently transposed to P300 decoding (Farahat et al., 2019).”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Farahat et al., 2019
Higher differences in the ERP between deviant and standard stimuli are reflected onto the saliency maps by positive and negative gradients.
“Therefore, as already obtained in Farahat et al. (2019), higher differences in the ERP between deviant and standard stimuli are reflected onto the saliency maps by means of positive and negative gradients.”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Farahat et al. (2019)
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- Authors
- Farahat, A., Reichert, C., Sweeney-Reed, C. M., & Hinrichs, H.
- Affiliations
- Otto-von-Guericke-Universität Magdeburg (DE)
- Leibniz Institute for Neurobiology (DE)
- Center for Behavioral Brain Sciences (DE)
- Corresponding author
- Farahat, Amr
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2019-10-23
- Volume
- 16
- Issue
- 6
- Pages
- 066010
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2968094935
- Cited by
- 92
- References
- 63
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 22 · 2023: 10 · 2022: 18 · 2021: 15 · 2020: 7
- FWCI
- 5.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (13GW0095D)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- EEG decoding, covert attention, saliency maps, convolutional neural networks, brain-computer interface, event-related potentials, P300, model interpretability, transfer learning, linear discriminant analysis, object selection, activation function comparison, dropout regularization, neural correlates
- MeSH terms
- Attention, Brain Mapping, Electroencephalography, Female, Humans, Male, Neural Networks, Computer
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