Learning Spatiotemporal Graph Representations for Visual Perception Using EEG Signals

Kalafatovich, Jenifer; Lee, Minji; Lee, Seong–Whan

doi:10.1109/tnsre.2022.3217344

Cited by 5 publications

(1 citation statement)

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“…In parallel, EEG signals from six auditory stimuli were classified for BCI applications utilizing classifiers based on random forest, multilayer perceptron, and decision tree architectures [ 32 ], wherein average accuracies of 91.56%, 89.92%, and 86.78% were reported, respectively. In addition, Kalafatovich et al implemented a two-stream convolutional neural network to classify single-trial EEG signals evoked by visual stimuli into two and six semantic categories [ 33 ]. They achieved accuracies of 54.28 ± 7.89% for the six-class case and 84.40 ± 8.03% for the two-class case.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Mwata-Velu,

Zamora,

Vasquez-Gomez

et al. 2024

Sensors

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This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain–computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

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Section: Related Workmentioning

confidence: 99%