ArticleFrontiers in human neuroscience2021
A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 34 citations in OpenAlex.
- Self supervised learning based emotion recognition using physiological signals.Frontiers in human neuroscience · 2024Article
- An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.Scientific reports · 2023Review
- Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.Scientific reports · 2023Article
- Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements.Sensors (Basel, Switzerland) · 2023Article
- Deep learning-based electroencephalic diagnosis of tinnitus symptom.Frontiers in human neuroscience · 2023Article
- On the relative importance of attention and response selection processes for multi-component behavior - Evidence from EEG-based deep learning.Neuroimage. Reports · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of different training strategies, and on the use of
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Registered trials
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