ArticlePloS one2020
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 63 citations in OpenAlex.
- Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models.PloS one · 2025Article
- Effect of the period of EEG signals on the decoding of motor information.Physical and engineering sciences in medicine · 2024Article
- Brain-computer interface in critical care and rehabilitation.Acute and critical care · 2024Article
- Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
- Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions.Journal of medical and biological engineering · 2022Article
- Status of deep learning for EEG-based brain-computer interface applications.Frontiers in computational neuroscience · 2022Review
- TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals.Brain sciences · 2021Article
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.Frontiers in neuroinformatics · 2021Article
- Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks.Brain sciences · 2020Article
- A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.Neural plasticity · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.