Jun 28, 2019 · In this paper, subband common spatial patterns using sequential backward floating selection is being proposed in order to classify motor-imagery-based EEG ...

Oct 22, 2024 · Brain Computer Interface translates EEG signals into control commands so that paralyzed people can control assistive devices.

The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective.

The results show that the proposed optimal feature selection and Neural Network based classification approach with overlapped frequency bands is an effective ...

15, NO. 10, OCTOBER 2019 5747. Soft Computing-Based EEG Classification by. Optimal Feature Selection and Neural Networks Muhammad Hamza ...

In this study, we have investigated the effect of the augmentation process on the classification performance of MI EEG signals instead of using a preceding ...

We propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals.

Jul 20, 2022 · Electroencephalography (EEG) signals are a combination of complex pattern sequences,that are periodic in nature.

A new type of EEG classification network, the separable EEGNet (S-EEGNet), is proposed based on Hilbert–Huang transform and a separable convolutional neural ...

This paper presents an innovative framework for efficient ES detection, providing coefficient and distance correlation feature selection algorithms.