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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MDPI AG
2025-08-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4779 |
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| author | Jiannan Chen Chenju Yang Rao Wei Changchun Hua Dianrui Mu Fuchun Sun |
| author_facet | Jiannan Chen Chenju Yang Rao Wei Changchun Hua Dianrui Mu Fuchun Sun |
| author_sort | Jiannan Chen |
| collection | DOAJ |
| description | 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 features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. |
| format | Article |
| id | doaj-art-ac2b88ac107d4c73b45e95c04ea8a470 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-ac2b88ac107d4c73b45e95c04ea8a4702025-08-20T03:02:56ZengMDPI AGSensors1424-82202025-08-012515477910.3390/s25154779Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal ClassificationJiannan Chen0Chenju Yang1Rao Wei2Changchun Hua3Dianrui Mu4Fuchun Sun5School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao 066004, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaIn 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 features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP.https://www.mdpi.com/1424-8220/25/15/4779brain–computer interfacedeep convolutional neural networkquadrotorshort time windowsteady-state visual evoked potentials |
| spellingShingle | Jiannan Chen Chenju Yang Rao Wei Changchun Hua Dianrui Mu Fuchun Sun Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification Sensors brain–computer interface deep convolutional neural network quadrotor short time window steady-state visual evoked potentials |
| title | Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification |
| title_full | Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification |
| title_fullStr | Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification |
| title_full_unstemmed | Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification |
| title_short | Steady-State Visual-Evoked-Potential–Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification |
| title_sort | steady state visual evoked potential driven quadrotor control using a deep residual cnn for short time signal classification |
| topic | brain–computer interface deep convolutional neural network quadrotor short time window steady-state visual evoked potentials |
| url | https://www.mdpi.com/1424-8220/25/15/4779 |
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