Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification

Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain...

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Bibliographic Details
Main Authors: Jiannan Chen, Chenju Yang, Rao Wei, Changchun Hua, Dianrui Mu, Fuchun Sun
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Sensors
Subjects:
brain–computer interface
deep convolutional neural network
quadrotor
short time window
steady-state visual evoked potentials
Online Access:https://www.mdpi.com/1424-8220/25/15/4779
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