Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Riyad, Mouad; Adib, Abdellah

doi:10.1007/978-3-030-51935-3_11

Cited by 32 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 presents the classification accuracy and kappa scores of each subject for several state-of-the-art MI-EEG algorithms employing a subject-specific approach on the BCI Competition IV-2a dataset. The suggested MBEEGNET and MBShallowConvNet, EEG-TCNet [ 30 ], both fixed and variable EEGNet [ 16 , 30 ], ShallowConvNet [ 16 ], and Incep-EEGNet [ 29 ] are the approaches compared. MBEEGNET and MBShallowConvNet, the proposed models, have an accuracy of 82.01% and 81.15%, respectively.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Altuwaijri

Muhammad

2022

Biosensors

View full text Add to dashboard Cite

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for convolutional neural network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method’s promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Other related architectures have been proposed: one is presented by M. Riyad et al in [ 29 ]. The first part of that model is the same as EEGNet, with two convolutional layers that act as a temporal and spatial filter, whereas the second part has the inception block.…”

Section: Introductionmentioning

confidence: 99%

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Altuwaijri

Muhammad

2022

Biosensors

View full text Add to dashboard Cite

show abstract

“…Due to the robustness and advanced performance implementation of EEGNet, several extended variations that combine domain knowledge are proposed. For example, Incep-EEGNet [Riyad et al, 2020] extended EEGNet by applying multi-head convolutions as an inception block with different kernel sizes (receptive fields) and pointwise convolution. By applying different narrow filter banks to the original signal with integration, FBCNet presents a hybrid approach with performance improvement on motor imagery (MI) tasks [Mane et al, 2021].…”

Section: Related Researchmentioning

confidence: 99%

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

Chen¹,

Teng²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

The development of brain-computer interfaces (BCI) has facilitated our study of mental representations in the brain. Neural networks (NNs) have been widely used in BCI due to their decent pattern learning capabilities; however, to our best knowledge, a comprehensive comparison between various neural network models has not been well addressed, due to the interdisciplinary difficulty and case-based study in the domain. Here, we tested the capabilities of common NN architectures in deciphering mental representations from electroencephalogram (EEG) signals, which were recorded in representative classification tasks. In this study, we: 1. Construct 20 mechanism-wise different, typical NN types and their variants on decoding various EEG datasets to show a comprehensive performance comparison regarding their EEG information representation capability. 2. Lighten an efficient pathway based on the analysis results to gradually develop general improvements and propose a novel NN architecture: EEGNeX. 3. We open-sourced all models in an out-of-the-box status, to serve as the benchmark in the BCI community. The performance benchmark contributes as an essential milestone to filling the gap between domains understanding and support for further interdisciplinary studies like analogy investigations between the brain bioelectric signal generation process and NN architecture. All benchmark models and EEGNeX source code is available at: https://github.com/chenxiachan/EEGNeX.

show abstract

“…Its original version, as well as several variants, have been recently applied to MI data. Among other EEGNet implementations, the Fusion-EEGNet [17] and the Incep-EEGNet [19] gained much attention and have been employed for different classification purposes, as shown in Table I. ShallowNet [11] also has a similar architecture, with the main difference being the use of another activation function for the last layers and the lack of depthwise convolutions.…”

Section: A Deep Learning Modelsmentioning

confidence: 99%

CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet

Zancanaro,

Cisotto,

Paulo

et al. 2021

Preprint

View full text Add to dashboard Cite

Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special effort to bring these systems out of the controlled lab environments. Hence, accurately classifying MI from brain signals, e.g., from electroencephalography (EEG), is essential to obtain reliable BCI systems. However, MI classification is still a challenging task, because the signals are characterized by poor signal-to-noiseratio, high intra-subject and cross-subject variability. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques, e.g., filter bank common spatial pattern (FBCSP), to extract subject-independent features and to increase the cross-subject classification performance of MI BCI systems. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We show-case the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with FBCSP in a 4-class MI classification over public datasets. To explore its cross-subject classification ability, we applied three different cross-validation schemes. From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification problems with respect to FBCSP. Moreover, in the future DynamicNet could be useful to implement new architectures to further investigate cross-subject classification for MI BCI in real-world scenarios.

show abstract

Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Cited by 32 publications

References 13 publications

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet

Contact Info

Product

Resources

About