Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Raza, Haider; Chowdhury, Anirban; Bhattacharyya, Saugat; Samothrakis, Spyros

doi:10.1109/ijcnn48605.2020.9207100

Cited by 31 publications

(19 citation statements)

References 38 publications

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“…For example, a Spiking neural network [30] based technique yielded an ACA of 75.62% for left vs. right hand MI task which is lower than the proposed methods of SW-LCR (ACA=80.02%) and SW-Mode (ACA=79.78%). Among the deep learning based approaches EEGNet achieved an ACA of 68.98% and NSL-EEGNet [38] achieved 70.6% which are significantly (p < 0.05) lower than the proposed SW-LCR and SW-Mode based approaches.…”

Section: Resultsmentioning

confidence: 77%

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Gaur

Gupta

Chowdhury

et al. 2021

IEEE Trans. Instrum. Meas.

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169

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Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain computer interface (BCI) systems. In this study two sliding window techniques are proposed to enhance binary classification of motor imagery (MI). The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows which is named as SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a dataset of healthy individuals and on a stroke patients dataset. As compared to the existing state-of-the-art the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients dataset for left vs. right hand MI with lower standard deviation. For both the datasets the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window based prediction of MI using SW-LCR and SW-Mode is robust against inter-trial and inter-session inconsistencies in the time of activation within a trial and thus can lead to reliable performance in a neurorehabilitative BCI setting.

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Section: Resultsmentioning

confidence: 77%

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Gaur

Gupta

Chowdhury

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

169

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“…The main advantage of hDL in respect to ML is less needed for human intervention [ 17 ]. However, the cost of this advantage could be summarized in two steps: the need for larger training sets [ 43 ] and the high computational efforts required [ 64 , 65 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

et al. 2021

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Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends. Results: Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency. Significance: To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

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“…The initial combination of 2D convolution and depth-wise convolution allows each temporal filter to learn spatial filters [ 55 ]. Meanwhile, the number of spatial filters learned from each feature map is controlled by a depth parameter [ 56 ]. After each convolution, batch normalization is performed to achieve model stability.…”

Section: Methodsmentioning

confidence: 99%

“…The addition of constraints to the model weights has been shown to minimize the complexity of the model [ 26 ]. One study improved the accuracy of identifying motor movements from the EEG data by 2% using the EEGNet model with regularization [ 56 ]. Consequently, tests were conducted to assess whether the addition of regularization to the model improves visual stimulus classification using a similar model [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

Komolovaitė

Maskeliūnas

Damaševičius

2022

Life

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Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.

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Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Cited by 31 publications

References 38 publications

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

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