An Ensemble Approach for Classification of Reach and Grasp Movements based on EEG Signals

Kanuparthi, Bhagyasree; Turlapaty, Anish C.; Gokaraju, Balakrishna

doi:10.1109/aipr52630.2021.9762070

Cited by 1 publication

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The preprocessing stage involved applying a bandpass filter, common average reference (CAR), and ICA. The classification task employed an ensemble K-nearest neighbors (KNN) algorithm, resulting in an accuracy rate of 85.13% [20]. In an alternative situation, the BCI competition IV and III datasets were subjected to pre-processing using the DWT and common spatial patterns (CSP).…”

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

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%