EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

Lawhern, Vernon J.; Solon, Amelia J.; Waytowich, Nicholas R.; Gordon, Stephen; Hung, Chou P.; Lance, Brent J.

doi:10.1088/1741-2552/aace8c

Cited by 2,295 publications

(2,238 citation statements)

References 109 publications

Supporting

Mentioning

1,940

Contrasting

Unclassified

Order By: Relevance

“…To understand class-specific spectral characteristics in the EEG recordings, we analyzed band powers in five frequency ranges: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14), low beta (14)(15)(16)(17)(18)(19)(20), high beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and low gamma .…”

Section: Visualizations Of the Spectral Differences Between Normalmentioning

confidence: 99%

“…These ConvNets exploit the hierarchical structure present in many natural signals. Recently, deep ConvNets trained end-to-end were, for example, able to more accurately diagnose skin cancer types from images than human dermatologists [9] and could segment retinal vessels better than human annotators [10].Deep ConvNets are nowadays also being applied to EEG analyses, such as decoding task-related information from EEG [11][12][13][14][15][16]. We have recently developed and validated the Braindecode toolbox1 for this purpose, and showed that the performance of deep ConvNets trained end-to-end is comparable to that of algorithms using hand-engineered features to decode task-related information.…”

mentioning

confidence: 99%

“…Deep ConvNets are nowadays also being applied to EEG analyses, such as decoding task-related information from EEG [11][12][13][14][15][16]. We have recently developed and validated the Braindecode toolbox1 for this purpose, and showed that the performance of deep ConvNets trained end-to-end is comparable to that of algorithms using hand-engineered features to decode task-related information.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister

Gemein

Eggensperger

et al. 2017

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

144

View full text Add to dashboard Cite

Abstract-We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ≈85% vs. ≈79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.I. I Electroencephalography (EEG) is widely used in clinical practice because of its low cost and its lack of side effects due to its noninvasive nature. It is important both as a screening method as well as for hypothesis-based diagnostics, e.g., in epilepsy or stroke. One of the main limitations of using EEG for diagnostics is the required time and specialized knowledge of experts that need to be well-trained on EEG diagnostics to reach reliable results. Therefore, a machine-learning approach that aids in the diagnostic process could make EEG diagnosis more widely accessible, reduce time and effort for clinicians and potentially make diagnoses more accurate.In recent years researchers have increasingly addressed the field of computer-aided EEG diagnosis. So far, the applications were mostly limited to specific diagnoses such as Alzheimer's disease regression, neural networks, and more. This large variety of used methods indicates that the search for the best decoding approach for diverse types of EEG diagnosis is still ongoing.To overcome the lack of large datasets representative of the variety of EEG-diagnosable diseases and the heterogeneity of clinical populations, the Temple University Hospital (TUH) has published an unprecedented public dataset of clinical EEG recordings [6]. From this dataset with over 16000 clinical recordings, the TUH Abnormal EEG Corpus with about 3000 recordings has been created specifically to foster the development of methods for distinguishing pathological from normal EEG. Due to its size and rich annotatio...

show abstract

Section: Visualizations Of the Spectral Differences Between Normalmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister

Gemein

Eggensperger

et al. 2017

2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

144

View full text Add to dashboard Cite

show abstract

“…As found out by Lawhern,7,8 with extremely less number of parameters, training is faster and overfitting is very less. As found out by Lawhern,7,8 with extremely less number of parameters, training is faster and overfitting is very less.…”

Section: Using Deep Learning Structurementioning

confidence: 95%

“…3 Few researchers also suggested some frameworks in analyzing the various methods in the prediction of epileptic seizures. 7,8 Weighted frequencies were calculated and used to make comparisons between normal and seizure signals using statistical methods like t-test. 5 Analysis of the IMF helped researchers in discriminating against the normal EEG and seizure EEG very efficiently, but the possibility of prediction some minutes or hour before the onset of seizure was not discussed.…”

Section: Related Workmentioning

confidence: 99%

A forecasting tool for prediction of epileptic seizures using a machine learning approach

Asharindavida

Hossain

Thacham

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

ECG and EEG signals are very helpful in the early diagnosis of epileptic seizures. The research focuses on analysis of ECG and EEG signals applying a deep learning technique to study early prediction of epileptic seizure. Signal processing methods like Empirical Mode Decomposition, spectral analysis, and statistical methods were used. The algorithms were implemented in MATLAB, and the EEG and ECG data were collected from Physiobank and EPILEPSIAE databases.In the window-based analysis of low-frequency spectral area of EEG signals, 78.5% of the cases displayed a significant change as the windows progressed and the onset of seizure was approached. The spectral area of IMF components indicated a possible seizure prediction in 68.9% of the analyzed cases. Considering signals from individual EEG electrodes, the least percentage of seizure prediction was indicated by signals from T4 and F4 electrodes (52.3% and 40.7%, respectively, for spectral peaks and 23.8% and 29.6%, respectively, for spectral area). The results of regression analysis show that prediction of seizures can be possible around 20-30 minutes prior to the actual occurrence of seizures. KEYWORDS Electrocardiogram (ECG), Electroensephalogram (EEG), Empirical Mode Decomposition (EMD), epileptic seizure, machine learning 1 Concurrency Computat Pract Exper. 2020;32:e5111.wileyonlinelibrary.com/journal/cpe

show abstract

Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis

Wadhera

Kakkar

Rani

2021

Emerging Technologies for Healthcare

View full text Add to dashboard Cite

EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

Abstract: Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.

Cited by 2,295 publications

References 109 publications

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

A forecasting tool for prediction of epileptic seizures using a machine learning approach

Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis

Contact Info

Product

Resources

About