Optimization enabled deep residual neural network for motor imagery EEG signal classification

Kumar, Dr T. Rajesh; Mahalaxmi, U.S.B.K.; MM, Ramakrishna; Bhatt, Dr Dhowmya

doi:10.1016/j.bspc.2022.104317

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…The BCI competition III and IV studies employed a linear adaptive filter for pre-processing purposes. Additionally, a common spatial discriminant analysis (CSDA) classifier was utilized, resulting in accuracies of 91.6% and 91.1% [27]. However, it is important to note that the existing studies have not conducted hardware testing of the developed CNN models.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Sultan,

Ijaz,

Waris

et al. 2023

Meas. Sci. Technol.

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Brain–computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms.

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Section: Literature Reviewmentioning

confidence: 99%

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Sultan,

Ijaz,

Waris

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Brain-computer interfaces (BCIs) offer direct thought-based control for individuals with disabilities, but accurate EEG signal classification is a significant challenge due to signal noise and interference. Developing practical signal processing and machine learning algorithms is crucial to enhance BCI accuracy, potentially improving the quality of life for those with impairments [12].…”

Section: Introductionmentioning

confidence: 99%

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

jain

2023

Preprint

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BrainWave Diagnostics, an emerging field, leverages electroencephalography (EEG) data for cost-effective and resource-efficient neurological disorder detection. Although EEGs are commonly used for neurological disease detection, their low signal intensity and nonlinear features pose analytical challenges. This review explores the use of high-performance computational tools, machine learning, and deep learning methods in diagnosing a range of neurological disorders, including epilepsy, Parkinson's disease, autism, ADHD, stroke, tumors, schizophrenia, Alzheimer's, depression, and alcohol use disorder. The increasing prevalence of neurological disorders and their resource burden underscores the urgency of these diagnostic advancements. Future research can consider multi-modal approaches, providing practical solutions for neurological disorder detection beyond EEGs, with potential applications in diverse signal analysis domains.

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Adaptive Beamforming Algorithm Based on Residual Neural Networks

Chen,

Wei,

Liu

et al. 2024

Circuits Syst Signal Process

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Optimization enabled deep residual neural network for motor imagery EEG signal classification

Cited by 8 publications

References 40 publications

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals

Adaptive Beamforming Algorithm Based on Residual Neural Networks

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